Embeddings and rerankers
Encoders and re-rankers appropriate to your language mix and domain.
Services · 05
RAG and chatbots grounded in your knowledge
Retrieval-augmented generation (RAG) is how assistants answer questions using your documents and tickets as evidence, instead of guessing from model weights alone. Databotiq builds internal assistants and customer-facing copilots where citations, permissions, and freshness matter as much as fluency.
At a glance
- Practice
- Agentic Knowledge Assistants
- Best fit when
- operators need answers from a private corpus where citations and access control are non-negotiable.
- Typical Rapid POC
- 14 days, fixed scope.
Problems we solve
The pains buyers describe to us first.
Generic chatbots hallucinate on proprietary details.
Static FAQs rot the day after launch.
Engineering teams underestimate chunking, access control, and eval work.
Nobody owns refresh when manuals and policies change weekly.
Approach
Our approach.
We implement hybrid retrieval (lexical + vector), re-ranking, and answer policies that force citations for factual claims. Access control is enforced at retrieval time so private docs never enter the model context for unauthorized users.
Technical depth
Evaluation that matches reality
We build question sets from real tickets and operator interviews, then measure groundedness, refusal quality, and latency under concurrent load. Not only BLEU-like proxies.
Tech (May 2026)
Named tools, not vague acronyms.
Specificity earns trust. The choices below reflect what we ship today, and they will evolve as new models and tools clear our internal evaluations.
Vector stores
Indexes sized to your corpus and update cadence, with hybrid lexical search.
Models
Chosen for instruction-following and citation discipline at your latency budget.
Where this fits
Industries and roles we ship for.
Manufacturing and field service
Manuals, bulletins, and work order history.
Internal IT and HR
Policy assistants with strict ACLs from your identity provider.
Customer help centers
Answers that must cite public docs only, with refusal on weak evidence.
Case pattern
Fifteen years of manuals and tickets, searchable with citations
This pattern is for teams where technicians ask the same questions across plants but answers depend on machine revision, region, and superseded bulletins. The assistant must cite sources, respect access control, and refuse when evidence is weak, because wrong torque is not a branding problem.
Read the case patternOutcome
What this means for you.
Operators get answers they can trust enough to act, because the assistant shows sources, admits uncertainty, and routes edge cases instead of improvising.
Further reading
Related practice notes and patterns.
FAQ
Questions buyers ask about agentic knowledge assistants.
Specifics on accuracy, deployment, integration, and the proof path. If something isn't covered here,ask us directly.
When is RAG better than fine-tuning?
When facts change frequently and you need citations. Fine-tuning can still help tone or small specialized tasks, but it is a poor substitute for a living document corpus.
How do you reduce hallucinations?
Ground answers in retrieved passages, require citations for factual claims, and use refusal policies when evidence is weak. We tune this empirically on your eval set.
How do permissions work?
The retrieval layer enforces ACLs from your identity provider or content system. If a user cannot read a doc in SharePoint, it cannot appear in context.
How often should we refresh embeddings?
On a schedule tied to document churn. Weekly for fast-moving teams, daily for some support corpora. We automate invalidation on document updates where APIs allow.
Can this sit inside our VPC?
Yes, when your security model requires it. We can self-host models for sensitive environments.
What is the proof path?
A Rapid POC on a slice of your corpus with side-by-side comparisons to your current search or support macros, plus latency and cost measurements.
See it on your data in 10 days.
We run a sandboxed Rapid POC so you can evaluate outputs, integrations, and risk before you fund production.