Generative AI & LLM Development
LLM features your users trust: grounded in your data, measured, and cheap enough to run at scale.
Overview
Adding an LLM to a product is easy. Making its answers accurate, safe and affordable is the real work. We build retrieval-augmented generation (RAG) over your documents and databases, AI copilots inside your apps, document and data extraction, and fine-tuned or distilled models when an off-the-shelf model is not enough.
Challenges we solve
- Answers that sound right but are wrong (hallucinations)
- Proofs of concept that never reach production
- Sensitive data that cannot leave your environment
- Token costs that make the feature unprofitable
What we deliver
Generative AI & LLM Apps capabilities
RAG and enterprise search
Hybrid search, re-ranking and citations over your documents, wikis, tickets and databases, with access control respected per user.
AI copilots in your product
Assistants that draft, summarise, explain and fill forms inside your web or mobile app, with streaming UI and feedback capture.
Document and data extraction
Turn invoices, contracts, forms and emails into structured data with confidence scores and a review queue for edge cases.
Fine-tuning and small models
Fine-tuned, distilled or open-weight models for narrow tasks where speed, cost or privacy matter more than generality.
Private and on-prem deployment
Deploy on your cloud account or on-prem with no training on your data, for regulated industries and strict data-residency needs.
LLM evaluation and monitoring
Offline test sets, online quality scoring, drift alerts and cost dashboards so quality never silently drops.
Process
How we work
- 01
Scope
Pick the use case, collect sample questions and agree on quality metrics.
- 02
Build
Data pipeline, retrieval, prompts and UI, measured against the test set.
- 03
Validate
Red-teaming, security review and a pilot with real users.
- 04
Scale
Production rollout with monitoring, caching and cost optimisation.
FAQ
Generative AI & LLM Apps: common questions
What is RAG and do we need it?
Retrieval-augmented generation lets a model answer from your own documents and data instead of its general training. You need it whenever answers must be specific to your business and up to date.
Will our data be used to train public models?
No. We use enterprise API terms that exclude training on your data, or private and open-weight models deployed in your own environment.
How do you reduce hallucinations?
Grounding in retrieved sources, citations, structured outputs, refusal when evidence is missing, and an evaluation set that catches regressions before release.
Related
Services that work well together
AI Agent Development
Custom AI agents and multi-agent workflows that act on your tools and data: support, sales ops, back office and internal copilots.
Learn moreAI Business Process Automation
Automate back-office work across email, WhatsApp, spreadsheets, CRM and ERP with AI workflows and clear human approval steps.
Learn moreSoftware Architecture & AI Readiness
Architecture reviews, cloud-native and event-driven design, legacy modernisation and AI readiness assessments.
Learn more
Ready to talk about generative ai & llm apps?
Tell us about it. In a free 30-minute call we will tell you honestly what AI can do, what it costs and how fast you can see results.