A chatbot that does not resolve
A generic model with no access to your data, answering what was already in the FAQ. The customer keeps pushing until they ask for a human.
"The bot answers, it just does not resolve. And it annoys people first."
Artificial Intelligence
AI that closes tickets, not AI that demos well.
Agents wired into your CRM, ERP and knowledge base. Scoped to your own data, a cited source on every answer, and human review wherever a mistake is expensive.
4–6 wks
from pilot to production
100%
of answers with a cited source
0
of your data training third-party models
Models and platforms we work with
ClaudeGPTGeminiLlamaAzure OpenAIAmazon BedrockZendesk AIHubSpot AISalesforce EinsteinLangChainPineconen8n
Why AI projects stall
The pilot impresses. The operation never sees it.
Four reasons explain why most AI initiatives never leave the slide deck.
Answers with no provenance
The AI states things confidently and nobody can check where it came from. One wrong answer on billing or a contract is expensive.
"We cannot put this in front of a customer without someone checking it."
Nothing to stand on
Knowledge scattered across PDFs, email threads and people. With no organised source, no model has anything to ground itself in.
"The information exists. It is just in four different places."
A pilot that never becomes routine
A pretty proof of concept with no CRM integration and no owner. Six months later nobody remembers it existed.
"We ran the pilot, everyone applauded, and it stopped there."
What we put into production
Three AI tracks. All wired into the systems you already run.
No side project. The AI goes inside the CRM, the ERP and the service desk your operation already uses.
Support
Triage & first levelResolve without transferring.
The agent classifies, searches the base and answers with a source. Whatever it cannot resolve reaches a human with context attached.
Deliverables for this track
- Knowledge base structured from what already exists
- Agent scoped to your data, with source citation
- Escalation rules by confidence and criticality
- Handoff with history and summary, so nobody repeats themselves
- Dashboard for deflection, CSAT and sourced-answer rate
Case · −41% in handling time
Commercial
Sales & RevOpsReps who stop typing.
Qualification, enrichment and logging happen automatically inside the CRM, so the team spends its time in conversations.
Deliverables for this track
- Lead qualification and enrichment at the top of the funnel
- Meeting summaries and opportunity updates without typing
- Pipeline prioritisation by intent signal
- Stagnation alerts and a suggested next step
- Everything written to the CRM, auditable by any manager
Case · automated qualification on first triage
Back office
Processes & documentsReading and checking without a queue.
Document extraction, checking against business rules and routing — with a human deciding what counts as an exception.
Deliverables for this track
- Structured extraction from invoices, contracts and forms
- Automatic checking against your business rules
- Exception queue for human review, never blind approval
- ERP integration so data lands without re-typing
- Audit trail for every agent decision
Case · +200k tickets processed through automation
How it works
Four phases. From use case to routine.
We start with the case that has the clearest return, not the one that demos best. Every phase has a success criterion agreed before it begins.
- 1Week 0
Diagnostic
We map where AI has measurable return in your operation, and where it would only add risk. You get the prioritised list with an impact estimate.
- 2Weeks 1–2
Base and guardrails
We organise the knowledge source and set the rules: what the agent may answer, what needs a source, what always goes to a human.
- 3Weeks 2–5
Measured pilot
Agent in production on a narrow scope, with an agreed metric and a comparison against the current process. If it misses the criterion, we adjust or stop.
- 4Week 5+
Expansion and operation
Rollout to the remaining cases, with a named owner, documentation handed over and indicators tracked.
Guardrails
What stops the AI from making things up.
Language models get things wrong. What changes the outcome is what you build around them — and that is part of the scope, not an add-on.
Scope
- Answers restricted to your knowledge base
- Explicit refusal when there is no source
- No access to data beyond what the task needs
- A test environment before anything reaches production
Traceability
- A cited source on every answer
- Full log of question, context and response
- Audit trail any manager can inspect
- Confidence score per interaction
Human review
- Automatic escalation on low confidence
- Human approval wherever a mistake is expensive
- Exception queue with a named owner
- No irreversible decision taken by the agent
Data
- Your data does not train third-party models
- Retention and residency defined in the contract
- Anonymisation of anything sensitive
- Aligned with GDPR and your internal policy
If a use case cannot have a cited source or workable human review, our recommendation is not to use AI on it. We would rather say that in the diagnostic than discover it later.
Results
Agents in production. Real operations.
Operations already running with AI
See all cases→Some of the companies we serve
Before you decide
What people ask before starting.
Language models get things wrong. That is why we restrict the scope to your data, require a cited source on every answer, and keep human review wherever a mistake is expensive. When the agent has no basis to answer, it says so and escalates — it does not invent.