Generative AI
Consulting Services

We provide generative AI consulting services to help organisations run foundation models on their own data and deploy them beyond the proof-of-concept stage. Being an official OpenAI Select Partner, our generative AI consultants work with RAG frameworks and vector databases such as Pinecone, Weaviate and pgvector, evaluate outputs using Ragas and Langfuse, and apply runtime safeguards with Guardrails AI and NeMo Guardrails, supporting teams in the USA, Singapore, India and the UAE. Moreover, we also consider relevant security, privacy and regulatory requirements, including SOC 2, ISO 27001, GDPR, HIPAA, the EU AI Act and India’s DPDP Act.

Trusted By Leading Enterprises

Data Scientists & AI Engineers Onboard

0 +

Custom AI Models Trained and Deployed

0 +

Autonomous AI Agents Deployed

0 +

Years of Experience

0 +

Industries Mastered

0 +

Average Client Rating on Clutch

0 /5

Clients Served Across 12+ Countries

0 +

Dextra Labs, A Generative AI Consulting Company

As an official OpenAI Select Partner, Dextra Labs is a generative AI consulting company whose consultants have hands-on experience building and deploying foundation models into production. Our approach begins with foundations of a reliable AI architecture that includes looking at where your data sits, how retrieval will behave against it, which model fits the latency and cost envelope, and who approves an output before it reaches a customer.

From there, we work across the components that make a production system function as a whole. We select foundation models across Claude, GPT, Gemini and open-weight options on evidence rather than preference, design retrieval augmented generation (RAG) pipelines on Pinecone, Weaviate or pgvector, and engineer the prompt, context and agent orchestration layers using LangGraph, CrewAI and AutoGen with MCP for structured tool access. Moreover, we fine-tune where it earns its cost against a measured baseline, and validate every system through evaluation frameworks such as Ragas rather than trusting that it works because it did well in the demo.

Once the system is live, the focus shifts to keeping it reliable under real workloads. Production brings its own challenges, from unexpected model behaviour to rising usage costs and stricter access requirements. Langfuse and LangSmith help make application behaviour visible, while runtime guardrails, access controls and cost governance provide the means to manage risks and resources. These are the controls that help an AI application remain manageable beyond launch, when reliability matters as much as functionality.

0 +

ChatGPT & LLM Applications Shipped

0 +

Org Brains & Context Layers Built

0 +

Global Delivery Hubs (USA, UK, Singapore, India)

0 +

AI Engineers & ChatGPT Developers

5 Signs Your Business Need Generative AI Consulting

You don’t necessarily need generative AI consultants because you lack ideas. You may need them because your pilots aren’t progressing, your costs are unpredictable, or your teams can’t agree on the next step. Let’s have a look at five common signs that mean you need GenAI consulting services.

Your Pilot Works But Nobody Will Approve Production

Your demo went well, everyone saw the potential, and then things stalled. Two quarters later, you’re still waiting for someone to approve production. The problem often isn’t the model. It’s that you don’t have clear evaluation criteria, an agreed owner or a defined approval process. Our generative AI consultants help you put these pieces in place so you can move from a promising pilot to a system you’re ready to run in production.

Your Token Bill Is Growing Faster Than Your Usage

Your AI cost has outrun the forecast, you can't attribute it to a feature or a team. Unlike traditional software licences, inference costs depend on how your systems are used, and without a cost model, you can end up with an unpleasant surprise just a few months after launch. With the right generative AI consulting services, you can identify where model routing, caching and model tiering will help you control costs without compromising performance.

You Aren’t Sure Which Model To Standardise On

New models keep coming out, every team has its own favourite, and you’re worried that choosing one today could mean rebuilding everything six months from now. But you need to look beyond demo performance. Which model meets your accuracy, latency, cost and data residency requirements? Through enterprise generative AI consulting, you can build a system that lets you switch models without reworking your entire architecture, giving you the flexibility to adapt as better options emerge.

Compliance Came Up After the Build, Not Before

You were already well into development when your risk, legal or audit teams raised questions about data residency, retention or human review. Now, you’re facing architectural changes that could have been avoided if those requirements had been considered earlier. In regulated industries, retrofitting these controls can put the entire project at risk. Working with a generative AI consulting company that understands production requirements helps you account for compliance from the beginning, rather than scrambling to address it before launch.

A Competitor Just Shipped Something You're Still Debating

You’ve spent months discussing an AI use case, and now your competitor has launched something similar. You can’t afford to keep waiting, but rushing into production without the right data, evaluation or governance can create bigger problems later. With the right generative AI consulting services and solutions, you can focus on the use case that matters most, move quickly and build the foundations needed to make it work reliably beyond the demo.

Stop bolting AI onto your business. Build the layer that understands it.

See how a custom Org Brain turns ChatGPT into software that actually knows how your company works.

Our Generative AI Consulting Services
We Offer At Dextra Labs

At Dextra Labs, our generative AI consulting services help you turn
business challenges into production-ready AI solutions, from
strategy and model selection to integration, governance and continuous optimization.

Our generative AI strategy consulting helps you turn business priorities into a practical roadmap, starting with use cases that make sense in terms of value, data availability and technical feasibility. We assess potential initiatives, identify what needs to happen first, and separate the use cases you can tackle with retrieval-augmented generation (RAG) now from those that need better data or integrations. You come away with a prioritised roadmap, clear owners, measurable success criteria and a rationale for every decision, including the initiatives we recommend putting on hold.

Model and platform selection is a constraint problem, not a preference. We benchmark models as per your accuracy targets, latency budget, context-window needs, residency rules and cost per million tokens, comparing frontier options such as Claude Opus 5.5, GPT-5 and Gemini 2.5 Pro against open-weight models like Llama and Mistral you can host yourself. The output is a documented selection with the trade-offs written down, including what we ruled out and why.

Retrieval quality depends upon the data behind them, so our readiness assessment examines your data before we design anything on top of it. This covers data coverage, freshness, access control, chunking strategy, embedding model choice and the vector store that fits your needs, whether Pinecone, Weaviate or pgvector. You receive a clear readiness report for each use case, along with mentioning the gaps you need to address and the projects we recommend holding off on until the foundations are in place.

Our GenAI consulting work turns your selected architecture into a working system built around your data, retrieval patterns and real-world edge cases. We build the RAG pipeline, refine the prompt and context strategy, implement reranking and retrieval logic, build an evaluation harness using Ragas to measure performance, and also plan for fallback behaviour when a model provides something unusable. Plus, if fine-tuning provides measurable gains over your baseline, we’ll recommend it. Even if it doesn’t justify the cost, we’ll tell you that too.

We build a working prototype against your live data early, because testing with sample inputs tells you very little. Once the approach is validated, our generative AI integration services connect the system to your existing tools through APIs, event pipelines, webhooks and service boundaries so outputs land inside the CRM, ERP or support tools your teams already use rather than in a separate interface nobody opens. Moreover, we also define authentication, error handling, rate limits and rollback points before anything reaches a production release.

Where a general model cannot reach the accuracy, domain language or cost profile a use case needs, our custom LLM development builds the adapted version as per what your use case need, covering fine-tuning, LoRA and parameter-efficient methods, instruction tuning, distillation to a smaller model for cost or latency, and private or VPC deployment where data residency require it. Before recommending the custom model work, we quantify the gain against the general-model baseline first so you can measure whether this investment is worth making.

As per our years of experience at Dextra Labs, governance works best when it’s part of the design from day one. So, our generative AI governance work is designed into the architecture rather than audited onto it afterwards. We define model access policy, data usage boundaries, human-in-the-loop review thresholds, evaluation standards and audit logging, while implementing runtime controls for prompt injection, PII redaction and output filtering through Guardrails AI, NeMo Guardrails or Lakera Guard. Moreover, considering the industry and operating regions, our generative AI consultants ensure the EU AI Act, GDPR, HIPAA, India’s DPDP Act, ISO/IEC 42001 and UAE data protection requirements.

Getting your system live is just a beginning. A generative system’s response quality and performance can drift, as your business data, users, and needs keep evolving. Our LLMOps services help you keep track of how the system performs from the first deployment. We monitor response quality, retrieval precision, latency, token costs and output drift against the baseline captured before launch by using Langfuse or Langsmith. Then, we use these traces to identify where prompts, retrieval logic or model routing need adjustment. Over time, your teams will gain the visibility and confidence to make informed decisions, fine-tune performance and manage the system without relying on us for every change.

Our generative AI development services take a validated design and turn it into production-grade software. We build the application layer, orchestrate models and APIs using LangGraph, CrewAI or AutoGen, develop the backend services, and create agent workflows with MCP for structured tool access, all integrated to your existing data and identity system. If you need an engineering team to build and deploy the solution rather than just advise on the approach, our generative AI development services page explains how we handle the build.

Our ChatGPT consulting services help you move beyond scattered individual use to a governed deployment on the GPT model family. We design the retrieval layer, system prompts, function-calling architecture and Assistants or Responses API integrations, along with the access and data controls your business needs. Plus, we also build an Org Brain context layer that connects ChatGPT to your organisation’s knowledge and business context, helping teams get more relevant answers grounded in the information they’re authorised to access. This way, you’ll receive more consistent, context-aware ChatGPT experience with the right controls and an audit trail, rather than a collection of browser tabs that everyone uses differently.

AI TECHNOLOGIES

GenAI Models Powering Our Generative AI Consulting Services

At Dextra Labs, we work with a range of generative AI models and technologies, selecting the right combination based on your business requirements, data, performance targets, and deployment constraints.

Large Language Models

LLMs are the engines behind text generation, summarisation, reasoning and classification, and they sit at the centre of most generative AI consulting engagements we run. We work across Claude, GPT, Gemini and open-weight models such as Llama and Mistral, selecting on your accuracy, latency and cost constraints rather than on a single provider. The best thing is that every output is grounded in your own data rather than the open web, so answers carry a source your team can check.

Natural Language Processing

NLP turns unstructured text into structured signals through extraction, classification and routing. Our generative AI consultants apply it across contracts, support tickets, claims and clinical notes, wherever meaning sits trapped in free text that no one has time to read. It is often the cheaper answer when a full language model would be overkill for the task.

Deep Learning

Deep learning uses layered neural networks to model relationships too complex for conventional algorithms, powering high-dimensional prediction, pattern detection and language understanding. We reach for it when simpler models have measurably plateaued, not before, because the added accuracy has to justify its cost in data and computation. This is where a mature generative AI consulting company earns its fee, by knowing when the heavier method is actually warranted.

Computer Vision

Computer vision interprets images and video, identifying defects, objects, documents and conditions a person would otherwise inspect by hand. We deploy it for quality checks on production lines, damage assessment in claims, and document capture at scale across high-volume operations. It fits precisely where the signal is visual and the volume defeats manual review.

Retrieval-Augmented Generation

RAG is the retrieval layer that lets a model answer from your corpus rather than from its training data, with a citation attached to every response. Our gen AI consulting work designs these pipelines on Pinecone, Weaviate or pgvector, tuned with the right chunking, embedding and reranking strategy for your documents. It is how most enterprise accuracy problems are actually solved, well before anyone needs to fine-tune a model.

Multimodal Models

Multimodal systems read images, audio and documents alongside text, which matters when the input arriving at your system is not clean prose. We build with them for use cases such as invoice and form processing, image-grounded support, and mixed-media analysis where a text-only model would miss half the signal. The model is chosen against the actual shape of your inputs rather than a benchmark.

Fine-Tuning & Model Adaptation

Where a general model cannot reach the domain language, output format or cost profile a use case needs, we adapt it through fine-tuning, instruction tuning and LoRA or parameter-efficient methods. Our generative AI consultancy quantifies the gain against the base-model baseline before committing, because adaptation is the easiest place on a programme to spend budget without moving a number. Where the data does not support it, we say so rather than bill for it.

AutoML

AutoML automates model selection, training and tuning for the structured prediction problems that sit alongside your generative work, with little manual intervention. We use it to move quickly on forecasting, scoring and classification tasks that do not need a language model at all. It keeps the generative and the classical parts of a solution in one coherent architecture rather than two disconnected efforts.

Speech & Voice Models

Speech models handle transcription, synthesis and real-time voice, and they are a specialist field where the best providers consistently beat general-purpose models. We integrate Deepgram, AssemblyAI, ElevenLabs and Cartesia depending on whether the priority is accuracy, latency or natural delivery. This lets voice use cases reach production quality rather than stalling at an impressive demo.

Synthetic Data Generation

Synthetic data lets us train, test and evaluate systems without exposing sensitive or regulated records, which matters most in finance, healthcare and the public sector. We generate privacy-compliant datasets that preserve the statistical shape of the real thing while removing the risk of using it directly. It is often what unblocks a use case that a data-protection review would otherwise stop.

Small Language Models

Small and distilled models carry narrow, repetitive tasks at volume and on latency-critical paths, at a fraction of the cost of a frontier model. We deploy them once a measured accuracy floor is in place, so cost savings never come at the expense of a wrong answer reaching a customer. For the right task, this is the difference between a system that is affordable to run and one that is not.

The Comprehensive ChatGPT Development Process
We Follow At Dextra Labs

A 6-step process works best here, enough to show rigor, not so much it drags.
Each ties back to the Org Brain so it reinforces your USP rather than reading like generic agency boilerplate.

Step 1: Discovery & AI Readiness Assessment

We start by understanding your business objectives, data landscape, existing systems, and where AI can create measurable value. We assess data readiness, security requirements, and use-case viability, then map which workflows and departments will form the foundation of your Org Brain.

Step 2: Architecture & Model Selection

We design the application architecture and select the right model for the workload, balancing reasoning quality, latency, cost, and compliance. This is where we define your RAG approach, integration points, and the context structure the application will draw on.

Step 3: Context Engineering & Org Brain Setup

We build the intelligence layer: department-scoped knowledge bases, retrieval pipelines, governed prompt templates, and access controls. This is the foundation every application plugs into, and what separates a Dextra Labs build from a generic chatbot.

Step 4: Development & Integration

We engineer the application layer, backend, LLM orchestration, and UX, then integrate with your CRM, ERP, databases, and internal systems so the application works with live business data inside your stack.

Step 5: Evaluation, Guardrails & Security Hardening

We test outputs against defined business criteria, implement guardrails and PII protection, and validate performance under real workloads, before anything reaches production.

Step 6: Deployment, Support & Continuous Optimization

Our AI experts at Dextra Labs will deploy to your cloud, private cloud, or on-premise environment, then monitor performance, optimize inference costs, refresh knowledge sources, and refine prompts as your business evolves.

Business Benefits of
Generative AI Consulting Services

You find out what will not work before you fund it

The most expensive generative AI outcome is a well-built system solving a problem that did not need solving. Our gen ai consulting services start with scoring and readiness, which is the cheapest stage at which to kill a bad use case. Saying no in week two costs a fraction of saying it in month six.

Your architecture survives a model change

Provider capability and pricing have moved materially every few months for three years running, and nothing suggests that stops. Building the retrieval, orchestration and evaluation layers so the model underneath is swappable means a better or cheaper option becomes an upgrade rather than a rewrite. Systems that hard-code a provider age badly.

The cost model is understood before the bill arrives

Inference cost scales with usage in a way licence cost does not, and most overruns trace to architecture decisions made without a cost model. We forecast token spend per use case at production volume and design routing, caching and model tiering against it. You get a number you can budget rather than a surprise in month four.

Governance exists before the auditor asks

In regulated sectors the controls decide whether a system ships at all, and retrofitting residency, retention, human review and audit logging onto a live deployment is the most expensive rework on a generative programme. Designing those in during architecture costs a fraction of adding them later. It also shortens the approval cycle rather than extending it.

Improvement becomes measurable rather than anecdotal

A generative system without a baseline cannot be defended, because a model degrading slowly looks exactly like one working well. We measure the process before we touch it, then report against that same number afterwards through Langfuse or LangSmith. That reading is what lets your team argue for the next phase with evidence.

Your team can run it without us

An engagement that leaves your organisation dependent on the people who built the system has created a liability rather than a capability. We hand over runbooks, evaluation criteria, documentation and the reasoning behind each architectural decision. The measure of a good generative AI engagement is how little you need to call us afterwards.

Generative AI Consulting Tech Stack

We use the best tools in AI to build, connect, and deploy powerful agents that work for your business.

DALLE 2

GPT-3

CLARITY

Curie AI

Jukebox

TensorFlow

PyTorch

Keras

AWS

Google Cloud

Azure

Docker

Kubernetes

Ansible

Python

JS

R

PostgreSQL

MySQL

Industries We Build Custom ChatGPT Solutions For

Our ChatGPT development services are tailored to the data, workflows, regulations, and customer expectations of each industry. Dextra Labs builds domain-specific applications that integrate with existing systems to automate work, improve decision-making, and provide faster access to business knowledge.

FinTech

Financial services demand accuracy, security, auditability, and strict regulatory controls. As a ChatGPT development company, Dextra Labs builds solutions for customer support, financial document analysis, compliance assistance, and advisory workflows, with secure data handling, traceable responses, and controlled access built into the architecture.

Retailers

Retail ChatGPT applications connect customer conversations with products, inventory, pricing, and purchase data. Our ChatGPT application development services help retailers build assistants for product discovery, personalized recommendations, order support, and customer service while reducing repetitive workload for retail teams.

Healthcare

Healthcare applications need to balance patient convenience with privacy, accuracy, and compliance. Our ChatGPT development services support appointment assistance, patient queries, document processing, and clinical knowledge retrieval, with controlled access and secure handling of sensitive healthcare information.

Supply Chain

Supply chain operations depend on real-time visibility across inventory, shipments, suppliers, and logistics. Our ChatGPT app development services help build assistants that query operational data, identify exceptions, surface shipment or stock issues, and help teams respond faster to disruptions.

Insurance

Insurance workflows involve large volumes of policies, claims, forms, and underwriting documents. Our ChatGPT developers build applications for claims intake, policy Q&A, document analysis, underwriting workflows, and customer service, helping insurers process information faster while maintaining compliance controls.

Manufacturing

Manufacturing teams need fast access to equipment data, technical documentation, maintenance procedures, and safety requirements. Businesses can hire ChatGPT developers from Dextra Labs to build troubleshooting, maintenance, process guidance, and knowledge retrieval assistants, including voice-enabled interfaces for technicians on the production floor.

E-Commerce

E-commerce applications need to connect conversational AI with product catalogs, inventory, pricing, and customer data. Our custom ChatGPT integration services connect these applications with commerce platforms and business systems to support product discovery, personalized recommendations, product Q&A, order support, and content generation.

Real Estate

Real estate teams manage large volumes of property listings, buyer enquiries, tenant questions, and lead qualification. Our ChatGPT integration services connect AI assistants with listing databases, CRM systems, and property workflows to answer questions, qualify prospects, and route high-intent leads to agents.

Information Technology (IT)

IT teams need faster support, efficient knowledge retrieval, and reliable access to technical documentation. As a GPT software development company, Dextra Labs builds helpdesk assistants, documentation search tools, and runbook-based systems that retrieve relevant information, automate routine support, and help engineers resolve issues faster.

What Makes Dextra Labs the
Best Generative AI Consulting Company
in USA, Singapore, India and UAE?

At Dextra Labs, our AI voice agent development services combine deep voice AI expertise, custom engineering, and production-focused implementation to build agents that perform reliably across industries and markets.

Production-First Engineering

Every voice agent we build is stress-tested against real call conditions before it reaches a live caller. We simulate background noise, regional accents, mid-sentence interruptions, and system failures during QA, because an agent that works in a quiet demo environment and breaks on a factory floor or a busy street is not ready for production. That rigor is what separates our ai voice agent development services from vendors shipping polished demos.

Compliance Built into Architecture

We do not bolt compliance after the build. SOC 2, HIPAA, GDPR, PCI DSS, and regional regulations like DPDPA and PDPA are addressed at the architecture stage, from how call recordings are stored and who can access transcripts to how PII is redacted in real time. Our enterprise ai voice agents are audit-ready from day one, not after a costly retrofit.

Global Presence, Local Context

With teams across the US, Singapore, India, and the UAE, we understand that a collections call in Texas follows different rules than one in Tamil Nadu, and a healthcare intake in London carries different disclosure requirements than one in Dubai. As an ai voice agent development company with true multi-market presence, we build regulatory, linguistic, and cultural nuances into each deployment rather than generalizing across them.

Full Lifecycle Ownership

We own the entire journey from conversation design and voice UX through integration, deployment, monitoring, and optimization, so you work with one accountable team instead of stitching together five vendors. That single thread of ownership is why businesses choose our ai voice agent services for businesses over fragmented agency setups that lose quality, context, and momentum at every handoff.

Multilingual from Day One

Our custom ai voice agent development services include multilingual capability built into the core architecture, not a translation layer bolted on top. We handle English, Hindi, Arabic, and other regional languages natively, including code-switching mid-sentence and local accent variations that off-the-shelf models routinely misinterpret, so a caller in Mumbai and a caller in Manchester both experience a conversation that feels local.

Continuous Improvement, Not Just Maintenance

Most vendors hand off a working agent and move on. Our voice ai solutions treat launch as the starting line. We retrain dialogue flows against live transcripts, optimize containment and resolution rates week over week, and surface call-level insights that show exactly where conversations succeed, where they break, and what we are doing about it.

Not sure whether to build custom or buy off-the-shelf

We’ll scope your use case honestly and tell you which fits, before you commit a dollar.

How We Work

Our Comprehensive
Generative AI Consulting Process

Every stage below produces something your team can evaluate and argue with. We do not separate strategy from engineering, because the recommendation and the thing that has to work are the same decision.

Step 1

Score the use cases

We start with the metric your leadership already cares about and work backwards to where generation could move it. Candidate use cases are scored on business value, data availability, technical feasibility and the visibility of the improvement. You finish this stage with a ranked shortlist and a written reason for every case we recommend dropping.

Step 2

Test data and platform readiness

Retrieval quality is capped by the corpus underneath it, so we assess coverage, freshness, access control and structure before designing anything. This stage also captures the baseline measurement that every later claim about improvement will be judged against. It is designed to surface blockers early and to say no when the data cannot support the use case.

Step 3

Select the model and architecture

With constraints established we test candidate models against your real accuracy, latency, residency and cost thresholds rather than against a benchmark table. The architecture decision covers retrieval strategy, chunking, embedding approach, orchestration and whether fine-tuning earns its cost against the general-model baseline. We document what we ruled out alongside what we chose.

Step 4

Design governance into the build

Approval workflows, access controls, human review thresholds, evaluation standards and runtime guardrails are defined before development begins. Depending on sector and geography this covers the EU AI Act, GDPR, HIPAA, India's DPDP Act, ISO/IEC 42001 and UAE requirements. Governance added after a system is live is the most expensive rework on a generative programme.

Step 5

Build and integrate

Implementation runs through controlled engineering milestones with rollback points defined before any release goes live. We connect retrieval, models and orchestration to your existing applications, data platforms and identity systems so outputs arrive where the work already happens. This keeps delivery measurable and keeps critical workflows out of harm's way.

Step 6

Validate against the thresholds

The system is evaluated against the criteria agreed in step two, not against an impression formed after launch. Testing covers accuracy, retrieval quality, response behaviour, latency, cost per outcome and failure modes under unusual input. Measuring against a pre-agreed number is what makes the improvement arguable rather than anecdotal.

Step 7

Hand over and keep measuring

We support the production release alongside runbooks, evaluation documentation and knowledge transfer, then instrument the system for drift, quality, latency and cost. Your team learns to read those signals and act on them. The goal is that you run and extend the system without calling us for every decision.

Meet the Senior Generative AI Consultants Behind Our Work

Our generative AI consultant team works directly on engagements rather than appearing at the kickoff and handing to delivery. You will know who is on your project before you sign.

12+ Years Shipping Production AI

Abhinav Srivastava

Abhinav has spent around twelve years building AI and machine learning systems that run in production, which is a different discipline from building ones that demo well. He leads engagements where the business case has to survive a finance review, working backwards from the number you need to move rather than forwards from the technology available. On most engagements he runs the cost model before anyone writes code, and he says so plainly when the payback does not hold. Clients tend to keep him on the call when the CFO joins.

Ask for Abhinav when your board wants a defensible number before it approves a generative AI budget
He will answer whether the use case pays back, what inference costs at production volume, and how long before it clears
In the first three weeks you get a scored use case shortlist and a cost model your finance team can audit line by line
Best fit for lending, insurance, energy and retail, where decisions are high volume and the impact is countable
He will push back if your data cannot support the accuracy your business case assumes

17 Year in enterprise transformation · Credentialed

Mudassar Pasha

Mudassar has spent around seventeen years inside enterprise transformation programmes, where the technology is rarely the part that decides the outcome. He runs delivery on engagements crossing several systems and several stakeholder groups, setting the sequence, the governance and the escalation path before the first sprint rather than after the first delay. As a credentialed trainer he also owns the capability side of an engagement, so your teams can carry the system once it is handed over. He has seen how programmes stall and designs around it in advance.

Ask for Mudassar when the distance between approval and production runs through several teams and systems
He will answer who needs to sign off, in what order, and where the programme is most likely to slow
In the first month you get a phased roadmap with named owners, decision gates and a working escalation path
Best fit for enterprises with legacy estates, multiple business units, or a regulated approval chain
He will push back if the sequence puts an integration ahead of the data work it depends on

Compliance and Security Standards
Our Generative AI Work Meets

Generative systems touch sensitive data and sit inside regulated workflows, so security and compliance are part of the architecture rather than a review stage at the end.

ISO 27001 Information security management

SOC 2 Security, availability, and confidentiality controls

ISO/IEC 42001 AI management systems (the newest AI-specific standard, a strong differentiator if you genuinely align to it)

GDPR EU data protection

HIPPA Healthcare data (where applicable)

EU AI Act readiness Risk-tiered AI governance

Your data. Your cloud. Your Org Brain.

Production-ready ChatGPT applications built by senior engineers across the USA, UK, Singapore, and India, owned entirely by you, with no vendor lock-in.

Frequently Asked Questions

What is Generative AI Consulting?

Generative AI consulting covers scoring where foundation models create value, proving they work on your data, designing the architecture, and governing the system afterwards. A full engagement spans readiness assessment, model selection, retrieval design and evaluation. Ours run from 2-week assessments to 16-week implementations, priced in fixed bands.

What is the difference between generative AI consulting and generative AI development?

Consulting decides what to build, which model fits and whether the data supports it. Development builds the system once those decisions are made. Most engagements need both, and our generative AI development services page covers the build side where a project needs engineering rather than advisory.

How much do generative AI consulting services cost?

Generative AI consulting costs depend on data condition, integration surface, regulatory load and who maintains the system afterwards. Our bands cover readiness assessments, strategy engagements, a 30-day proof of value, production pilots, full implementations and managed optimisation. Every band is a fixed-scope price, listed above on this page.

How long does a generative AI consulting engagement take?

A readiness assessment takes 2 to 4 weeks and strategy work 3 to 5. The 30-day proof of value runs 4 weeks. A production pilot takes 6 to 10 weeks, a full implementation 8 to 16. Regulated environments and multi-system integrations sit at the top of each range.

How do I choose a generative AI consulting partner?

Check four things: whether they own delivery or hand off after the recommendation, whether they name the consultants on your engagement, whether production references exist, and how they handle your data and model keys. Ask who signs off on accuracy and what happens when the model is wrong.

Do I actually need generative AI consulting?

Not always. A single well-scoped drafting or summarising task on clean data your team already owns is often faster built in-house. Generative AI consultants earn their cost when the work crosses systems, touches regulated data, needs an audit trail, or has to keep running after the builders leave.

Which generative AI model should an enterprise use?

No single model wins across every use case, which is why we test candidates against your accuracy, latency, residency and cost thresholds rather than a benchmark table. Claude, GPT and Gemini each lead on different axes. We build so the model stays swappable without rewriting the system.

How do you stop a generative AI system giving wrong answers?

Wrong answers are usually a retrieval problem rather than a model problem. We ground outputs in your own documents, score retrieval quality with Ragas, add runtime controls through Guardrails AI or NeMo Guardrails, and route anything below a confidence threshold to a human. Evaluation runs continuously, not once.

Where can I find generative AI consultants near me?

We deliver from Singapore, India, the United Arab Emirates and the United States, with consultants staffed to your business hours rather than ours. Our headquarters is at 20 Cecil Street, Singapore. We also deliver for clients across the United Kingdom and Europe from our Singapore and India teams.

Which industries benefit most from generative AI consulting?

Financial services, supply chain, manufacturing, healthcare, retail and agriculture carry the clearest returns, because each runs high volumes of document and language work that already exists in written form. We hold case studies in renewable energy forecasting, lending and underwriting, and multilingual customer service for D2C retail.

Generative AI Consulting Services