Generative AI development: LLM integration, retrieval architectures and evaluation — from idea to an application running on your own data.
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, revDSG, and Swiss/EU 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.
The AI Sprint builds on your own data rather than sample data — fixed scope, fixed price, code and hosting yours.
See the AI SprintGrounded 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.
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, revDSG, SOC 2 Type II, 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.
A Swiss entity you can hold responsible, with delivery centres in India. Working hours overlap with CET core hours, so coordination, reviews and demos happen the same day.
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.
