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Solution
AI assistants trained on your own operational knowledge
A general chatbot is a novelty. An assistant that knows your price list, your policies and who is allowed to see what becomes part of the operation.
Résultats
- First-line questions answered instantly
- Knowledge available without asking a colleague
- Consistent answers across the team
- Escalation to a human when confidence is low
Solution
Cas d'usage
Internal knowledge assistant
Policies, procedures and product data on demand.
Customer support assistant
Grounded answers with handover to an agent.
Operational agent
Taking an action in your systems after confirmation.
Ce que nous livrons
- Retrieval over your content
- Permission-aware answers
- Conversation logging and review
- Confidence thresholds and escalation
- Ongoing evaluation against real questions
Who this is for
- Support and internal teams answering the same questions from documents all day
- Businesses with substantial written knowledge that is technically searchable and practically not
When this is the wrong choice
- Public-facing advice where a wrong answer carries legal or clinical risk without human review
- Organisations with no maintained source content — an assistant over stale documents spreads stale answers
Problems this solves
Repeat questions
A large share of inbound tickets have documented answers. Grounded assistants deflect those and escalate the rest.
Onboarding takes months
New staff need someone to ask. An assistant over internal documentation shortens that considerably.
Answers without provenance
Staff will not trust an assistant that cannot show where the answer came from. Citations are non-negotiable.
Architecture and integration
- Vector retrieval over chunked, permission-aware content so users never see documents they could not otherwise open.
- Citation of source document and section with every answer.
- Refusal and escalation paths when retrieval confidence is low.
- Feedback capture on every answer, feeding a review queue for content owners.
How delivery runs
- 01
Content audit
We identify what is current, what is contradictory and what must be excluded before indexing anything.
- 02
Grounded retrieval build
Answers are generated only from retrieved source passages, with the citation shown to the user.
- 03
Escalation design
The assistant hands off to a human with full context rather than guessing when confidence is low.
- 04
Measured pilot
Deflection rate, correction rate and satisfaction tracked against a pre-launch baseline.
What moves the price
Content state
Well-maintained documentation is quick to index; contradictory files need an editorial pass first.
Permission complexity
Per-role document visibility adds real design work to retrieval.
Channel count
Web widget, internal chat tool and email each need their own integration.
Where projects go wrong
Confident wrong answers
Without grounding and citations, an assistant invents plausible policy. Retrieval-only answering prevents most of it.
No content owner
Answers decay with the source material. Someone must own the corpus.
FAQ
Questions fréquentes
How do we stop it inventing answers?+
Answers are grounded in retrieved sources, cited, and refused when confidence is low. We also test against a fixed question set each release.
Can it respect who is allowed to see what?+
Yes. Retrieval is filtered by the user's permissions before the model sees anything.
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