AI Development Services

Generative AI helps automate tasks such as drafting, summarisation, and information synthesis that traditionally require human reviewers, provided outputs are grounded and validated. Our artificial intelligence development solutions include retrieval-based systems built over your own data, output constraints through Guardrails AI, and model selection across Claude, GPT, and Gemini based on workload requirements.

Agentic AI extends beyond generating responses by enabling systems to plan multi-step tasks, interact with tools, and determine what action to take next. Our AI engineers orchestrate agents using LangGraph, AutoGen, or CrewAI, integrate tools through MCP, and configure confidence thresholds to hand control back to human teams when tasks fall outside defined trust boundaries.

RAG enables generative AI systems to retrieve information from your own data before generating responses, helping keep answers grounded in enterprise knowledge and traceable to their sources. Through our AI solution development process, we build retrieval pipelines using pgvector, Pinecone, or Weaviate, apply Cohere Rerank to improve retrieval relevance, and evaluate retrieval quality with Ragas before responses reach users.

RPA automates repetitive, rule-based activities such as clicking through applications, moving files, and submitting forms, leaving decisions that require judgement to AI models. We combine RPA bots with machine learning and generative AI components, allowing models to handle decision-making while bots execute the resulting actions across existing business workflows.

Computer vision enables AI systems to interpret images and videos and turn them into actionable data, from detecting production-line defects to extracting information from insurance claims. As an AI development agency, Dextra Labs builds and trains computer vision models on domain-specific data using PyTorch, then deploys them across cloud or edge environments based on latency requirements and where visual data needs to be processed.

NLP enables AI systems to understand and process unstructured business information found in contracts, support tickets, emails, policies, and forms. Our AI development team uses transformer models, Hugging Face, and spaCy to build solutions for text classification, entity extraction, and information routing, accounting for the terminology and language specific to your business.

Data science helps establish whether an AI system is delivering measurable improvements against the business outcomes it was designed to achieve. We incorporate pre-deployment baselines, time-based validation, and post-deployment monitoring through Evidently to measure model performance against existing processes rather than relying solely on training accuracy.

Edge AI processes data and runs model inference directly on the devices where information is generated, enabling real-time decisions across vehicles, factories, and remote assets. Our AI development solutions use techniques such as quantisation and distillation to optimise models, deploy them through containerised runtimes, and maintain cloud connectivity for retraining as new data comes back from deployed devices.

Sentiment analysis helps AI systems identify tone, intent, and urgency in customer interactions, providing the context needed to decide whether a message requires an automated response or human intervention. We fine-tune sentiment and intent classifiers on your labelled customer interactions, helping them interpret business-specific language more accurately than generic models trained on product reviews.

Explainable AI makes model predictions easier to interpret and audit, particularly when decisions affect regulated workflows in finance, healthcare, and public services. Dextra Labs integrates SHAP and LIME into relevant AI systems to identify the features influencing each prediction and the thresholds behind specific decisions, giving teams greater visibility into model behaviour.

Green AI focuses on achieving the required model accuracy while keeping compute consumption and inference costs under control. Our AI solutions development approach prioritises smaller models wherever accuracy permits, routes workloads to the most cost-effective capable model for each request, and monitors token usage and GPU expenditure through Langfuse to keep operational costs visible.