Custom LLM & RAG Solutions
Assistants, copilots and knowledge tools grounded in your own documents and data.
A general model knows the world. It does not know your contracts, your product manuals or your support history. Retrieval-augmented generation connects the two, so an assistant answers from your sources and shows where the answer came from. We build these systems for customer support, internal knowledge, sales enablement and document-heavy operations.
Quality depends on the retrieval pipeline and the evaluation set more than on the model. That is where we spend the effort.
- Permissions are enforced at retrieval, so a user never sees content they couldn't open directly.
- Every answer cites its source, which builds trust and makes errors easy to spot.
- The evaluation set is built with your experts and runs automatically, so quality is measured, not assumed.
- A bank, insurer or telecoms operator whose support teams search thousands of policy documents by hand.
- An engineering or manufacturing group with decades of manuals, drawings and maintenance records.
- A legal or professional services function inside a group that needs answers with sources, not summaries.
- Staff ask colleagues because the intranet search returns nothing useful.
- A vendor chatbot was trialled and answered confidently and wrongly.
- Documents carry access restrictions that a general assistant would ignore.
- Answer quality has never been measured against a set of real questions.
What is included.
- 01
Use case and data scoping
Which questions the assistant must answer, from which sources, for whom.
- 02
Ingestion pipeline
Document parsing, chunking, metadata and permissions-aware indexing.
- 03
Retrieval and generation
Hybrid search, reranking, prompt design and citation of sources.
- 04
Evaluation
A test set of real questions with graded answers, run on every change.
- 05
Interface and integration
Chat, in-app assistant, Slack or Teams bot, or an API for your product.
- 06
Deployment
Hosted in your cloud with monitoring, cost controls and access management.
Four steps, no surprises.
- 01
Scope
Questions, sources, users and success measures agreed.
- 02
Ingest
Pipeline built and indexed with permissions and metadata.
- 03
Tune
Retrieval and prompts iterated against the evaluation set.
- 04
Deploy
Launch in the chosen interface with monitoring and feedback capture.
From first meeting to steady state.
- 01Weeks 1 to 2
Scope
Questions, sources, users, permissions and success measures agreed.
- 02Weeks 3 to 8
Ingest and tune
Pipeline built and indexed with permissions and metadata, then retrieval and prompts iterated against the evaluation set.
- 03Weeks 9 to 12
Deploy
Launch in the chosen interface with monitoring, cost controls and feedback capture.
- Answer accuracy on the evaluation set built with your experts.
- Share of answers with a correct citation.
- Time to answer for the users in scope, before and after.
- Permission violations detected in testing and in production.
- AI engineering lead
- Retrieval engineer
- Data engineer
- Product designer
- Domain expert liaison
- Retrieval pipeline and index.
- Evaluation set with results.
- Deployed assistant in the agreed interface.
- Monitoring and cost dashboard.
- Documentation and hand-over.
Assistant builds are fixed scope after a scoping phase of one to two weeks. A first production release typically takes six to twelve weeks. Ongoing improvement, content updates and monitoring run on a monthly retainer.
Workflow Automation & AI Agents
Agents and automations wired into CRM, ERP and support tools with human approval where it matters.
CybersecurityAI/LLM Security Assessment
Prompt injection, data leakage and model abuse testing for AI features, assistants and agents.
AI Consulting & AutomationData Engineering & MLOps
Pipelines, feature stores, model deployment and monitoring so AI keeps working after launch.
Custom LLM & RAG Solutions, in plain terms.
The one that meets your quality, cost and data residency needs. We benchmark options on your evaluation set rather than assuming.
Grounding, citations and guardrails reduce it substantially. The evaluation set measures how often it happens so you know the real rate.
Yes. Structured data and live system queries can be added as tools, which moves toward an agent. See the automation service.
In your cloud environment or a vector database you control, with enterprise model agreements that exclude training on your data.