Lead qualification
Incoming enquiries enriched, scored against your own criteria, and routed to the right owner with a written rationale a salesperson can read in five seconds.
Services
AI that does a specific job, inside your systems.
We build agents around a named workflow rather than around a model. The scope is a task you can describe in a sentence, the integration is with the tools your team already uses, and the boundary where a human takes over is designed before anything is written.
The problem we are usually brought in for
The common failure is not that the model is bad — it is that the project has no edges. A general-purpose assistant is bolted onto the company, nobody owns it, it has no access to real data, its answers cannot be audited, and within a quarter it is quietly switched off. Meanwhile the actual cost sits in a specific, boring, high-volume workflow nobody scoped.
- A high-volume manual step that scales linearly with headcount.
- Answers that live in documents nobody can find quickly enough.
- Enquiries that sit unqualified until someone gets to them.
- Pilots that never made it out of a demo because nothing was integrated.
What we build
Customer support
First-line answers grounded in your documented knowledge, with explicit escalation to a human the moment confidence or policy says so.
Internal knowledge assistants
Retrieval over your own documents with citations, permission-aware access, and honest 'I do not know' behaviour instead of confident invention.
Document processing
Structured extraction from invoices, contracts and forms into validated records, with a confidence threshold below which a person reviews before anything is written.
Workflow orchestration
Multi-step automations that read from and write to your CRM, ERP, ticketing and messaging, with retries, idempotency and an auditable trail.
Evaluation and monitoring
A test set drawn from your real cases, tracked quality metrics, and alerts when behaviour drifts. Without this an agent is unmaintainable.
How the work runs
Workflow selection
We map candidate workflows by volume, repetitiveness and cost of error, then pick one. Starting with several at once is the most reliable way to finish none.
Data and access review
What data exists, in what state, under whose permission. Most AI projects fail here, and it is far cheaper to find out in week one.
Boundary design
We define what the agent may decide alone, what it must propose for approval, and what it must never touch — before implementation, in writing.
Pilot on real cases
A narrow slice running against genuine historical data, measured against how the team performs today rather than against a demo.
Integration and rollout
Into the real systems, with logging, rate limits, cost controls and a documented rollback. Rollout is staged, never a switch flip.
What you should expect
Outcomes below are described qualitatively on purpose. We do not publish performance figures we cannot attribute to a named, consenting client.
- A specific workflow that no longer scales linearly with headcount.
- Faster first response on the queue you chose to automate.
- Decisions that can be audited, because every action is logged with its reasoning.
- A clear, documented boundary between machine action and human approval.
- An evaluation set that makes future changes safe to ship.
Related solutions
Customer Operations
Answer customers faster without adding headcount, and without pretending a bot is a person.
Process Automation
Remove the manual steps between systems that quietly consume a department's week.
Data & Analytics
One version of the numbers, available to the people who have to act on them.
Related use cases
AI Lead Qualification
Score and route incoming enquiries against your own criteria, with a written rationale attached.
AI Customer Support
First-line answers grounded in your documentation, with citations and designed escalation.
Internal Knowledge Assistant
Let your team find the answer that already exists, with permissions respected and sources shown.
Document Processing
Extract structured records from invoices, contracts and forms, with a review threshold before anything is written.
Frequently asked
Will the agent make decisions without us?
Only where you have explicitly said it may, and we default to the conservative side. Anything with financial, contractual or legal consequence is designed as a proposal a person approves. That boundary is written down during design and is testable, not a matter of trust.
What happens to our data?
Data handling is agreed before any integration: which systems are read, what is sent to a model provider, what is retained and for how long, and what is excluded outright. Where the requirement is that nothing leaves your perimeter, that constrains the model choice, and we will say so up front rather than after signing.
Which model do you use?
Whichever fits the task, the data-handling requirement and the budget — and we design so that it can be replaced. Model choice moves faster than any project timeline, so treating it as a swappable component rather than a foundation is a technical requirement, not a preference.
How do we know it is actually working?
Because we agree the measure before we build. Usually it is a comparison against your current process on the same cases: how many were handled without escalation, how many were wrong, and how long each took. If we cannot define that measure for a workflow, that is a strong signal not to automate it yet.
Next service
SaaS Products
From product architecture and MVP to a multi-tenant platform you can actually operate.
