01
Discovery
We inventory sources, access rules, update cadence, and the questions people actually ask. Thin or stale corpora get fixed before model work begins.
Service
LLM Applications
LLM applications we ship are grounded in your documents and systems—assistants, search over knowledge bases, and internal copilots—with permissions that match how your organization already works.
Problems this work usually addresses
- Staff waste time hunting for the latest policy or SOP
- Generic chatbots invent answers outside your corpus
- Leadership wants AI help without leaking restricted content
Who benefits most
Companies with a usable document corpus and clear roles who need answers tied to sources, not freeform chat.
Typical outcomes
- Ground answers in your own knowledge base
- Control access with roles and auditability
- Measure quality before you scale usage
Technical approach
02
Design & architecture
Retrieval, chunking, and citation patterns are designed first. The UI shows sources so users can verify.
03
Build, test, and handoff
We evaluate answer quality on a fixed question set, then harden rate limits, redaction, and admin controls.
Architecture considerations
Ingestion pipelines normalize documents into a searchable store. Query time assembles context under the caller’s permissions. Responses include source references. Admin tools manage sync and blocklists.
Security
Document ACLs are enforced before retrieval. Prompts never include data the user cannot already access. Admin actions are logged.
Scaling
Index rebuilds and incremental sync keep the corpus fresh. Caching covers repeated queries. We tune chunk sizes against measured answer quality, not guesswork.
Technology stack
Next.jsVector or hybrid searchLLM APIsObject storageAuth rolesEvaluation notebooks
Common challenges
- • Duplicate or conflicting documents
- • Users expecting certainty where the corpus is silent
- • Latency when context windows grow unchecked
Frequently asked questions
Do answers always cite sources?
Yes for production knowledge assistants. If the system cannot find support in the corpus, it should say so rather than invent an answer.
Can this connect to Notion, Drive, or Confluence?
Yes when those systems have stable APIs and clear ownership. Connectors are built per source with sync status visible to admins.
Related reading
Related services
AI Development
Production-ready AI features, model integrations, and LLM applications designed around real workflows—not demos.
AI Agents
Task-oriented agents with tools, memory, guardrails, and human checkpoints for real business work.
API Development
Clean APIs and integrations that connect products, CRMs, payment systems, and internal tools.
Relevant projects
Want an assistant that stays inside your knowledge base?
Tell us what sources matter, who can see what, and which questions burn the most time today.
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