Generative AI development for enterprise — RAG pipelines, LLM integrations, prompt engineering services, and production-ready generative AI solutions.
Generic LLM wrappers don't survive contact with real business requirements. These are the obstacles we solve every engagement.
Generic models confidently produce wrong answers. Without grounding, retrieval, and output validation, GenAI is a liability.
Sending proprietary data to public LLMs breaches data governance policies under GDPR, HIPAA, and UAE data residency rules.
Naive LLM integrations blast full context windows on every call, blowing latency and cost budgets at scale.
Jupyter notebook demos running on sample data bear no resemblance to the infrastructure needed for production workloads.
Connecting LLMs to internal knowledge, data warehouses, auth systems, and existing APIs requires significant engineering.
Building directly on one LLM provider's SDK creates fragility — model deprecations, price increases, and capability gaps.
We engineer GenAI systems to production standards: retrieval pipelines that ground answers in your data, evaluation frameworks that measure quality continuously, and infrastructure that scales without breaking cost budgets.
Design document ingestion pipelines, chunking strategies, metadata schemas, and hybrid retrieval (vector + keyword) for maximum answer relevance.
Build structured prompt templates, few-shot examples, and evaluation harnesses that measure answer quality, faithfulness, and latency continuously.
Select the right base model for your use case; fine-tune or instruction-tune where pre-training gaps exist, using efficient methods (LoRA, QLoRA).
Build streaming APIs, caching layers, fallback chains, and monitoring dashboards to serve GenAI at enterprise scale.
Bring the use case — we'll tell you whether an LLM, RAG, or fine-tune is the right approach, and what it'll actually cost to run.
Request Your ReviewGrounded Q&A systems over internal documents, PDFs, wikis, and structured data with source citations and confidence scores.
Structured content workflows (proposals, reports, product descriptions) with brand voice enforcement and output validation.
Domain-specific chat assistants with memory, session state, escalation paths, and integration with your existing support stack.
Information extraction, contract analysis, invoice processing, and regulatory document review at scale.
Code review copilots, documentation generators, refactoring assistants, and internal developer tools trained on your codebase.
Custom models fine-tuned on your proprietary data for tasks where general LLMs under-perform: medical, legal, financial, technical.
Structured pilot-to-production process with quality gates at each stage.
A management consultancy built a RAG system over 50,000 engagement documents. Analysts retrieve relevant case precedents in seconds rather than hours. Automated evaluation runs nightly to catch retrieval drift.
A wealth management firm automates regulatory report generation using a fine-tuned model trained on their reporting templates. Output requires only light human review before submission.
An e-commerce retailer generates SEO-optimised product descriptions for 200,000 SKUs in 4 languages using a fine-tuned brand-voice model. Time to catalogue new products dropped from 3 days to 4 hours.
We build evaluation harnesses before writing application code. Quality is measured, not assumed.
Data minimisation, PII redaction, and audit logging are engineering requirements, not afterthoughts. GDPR, HIPAA, SOC 2, EU AI Act aligned.
We build on abstraction layers so you can swap LLM providers as the market evolves — no application rewrites.
Context compression, caching, model routing (cheap model first, expensive model on fallback), and batch inference keep costs predictable.
Prompt libraries, evaluation datasets, fine-tuned model weights, and infrastructure code all transfer to you at handover.
Teams globally — same-day responses and workday overlap regardless of your timezone.
RAG is usually the right starting point: faster to build, easier to update, and auditable (you can show which document produced each answer). Fine-tuning makes sense when you need the model to produce outputs in a specific format or style that RAG alone can't achieve, or when you need significant latency reduction. We'll recommend the right approach after reviewing your use case and data.
We architect systems to minimise data exposure. For RAG, only the most relevant document chunks are sent to the LLM — not your entire corpus. For sensitive use cases, we can deploy open-source models (Llama 3, Mistral) entirely within your infrastructure so data never leaves your boundary. We also implement PII redaction before any LLM call where needed.
We build evaluation frameworks using tools like RAGAS, DeepEval, or custom harnesses that measure answer accuracy, faithfulness (is the answer grounded in the retrieved documents?), and relevance. These run automatically on every deployment, so quality drift is caught before users are affected.
We build on LLM-agnostic abstraction layers (LiteLLM or custom) so model swaps require configuration changes, not application rewrites. We also implement model routing so cheaper models handle simpler queries, reducing dependency on any single provider.
Yes. We build connectors for SharePoint, Confluence, Notion, Salesforce, SAP, and any system with an API or data export. For real-time use cases we implement change-data-capture pipelines that keep the knowledge base current as your source systems update.
A generative AI consulting engagement starts with a use-case feasibility review — we assess your data, infrastructure, and compliance constraints before recommending an approach. From there, we scope the architecture (RAG, fine-tuning, or agentic), select the LLM stack, and define the evaluation framework. Most consulting engagements run 2–4 weeks and produce a scoped delivery proposal, architecture diagram, and cost model your team can act on immediately.
RAG development is the right choice when your answers need to be grounded in current, proprietary documents — knowledge bases, policies, product data, or live reports. Custom LLM workflows fit when you need multi-step reasoning, conditional logic, tool use, or process automation that a single retrieval call can't handle. Most production generative AI solutions combine both: a RAG layer for knowledge retrieval and an orchestration layer for workflow logic. We'll recommend the right architecture after reviewing your use case.
