Start with the decision, trace back to the source
A useful workflow is not an AI that does everything. It is a repeatable path from an authorised record to a review-ready action, with uncertainty visible and a named person responsible for the decision.
How we decide, every time
Every engagement starts with the same question: which is the smallest reliable option — buy a specialist product, enable a feature already included, configure the current system, integrate a missing step, or build something new? We document which option wins and why.
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Buy
A named vendor product already solves this: we help you choose and buy it.
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Enable
Your platform already includes it: we switch on what you're already paying for.
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Configure
The capability exists but needs real configuration work to fit how you run.
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Integrate
Two systems need to work together through a supported, official API.
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Build
Nothing suitable exists: we build something new, only once every other option is ruled out.
Where a recommendation would earn us a referral or reseller fee, we disclose it before the recommendation. A native feature or existing supplier can be the correct answer.
Compare the three assessment routes →Five terms, used precisely
These labels describe the work in front of us. They are not interchangeable, and no label removes the need for a named owner and a clear hand-off.
- Rule-based workflow automation
- A defined trigger follows a defined route: receive a form, check a known field, create a task or send the approved next step.
- Business process automation
- A repeated business process is mapped end to end, including the people, systems, exceptions, controls and output that connect its steps.
- RPA
- A software robot performs screen-based steps in an existing system. We assess the system and its available routes before proposing any approach.
- AI-assisted decision support
- An AI component can classify, extract or draft from the material it is given. It prepares information; the named person keeps the judgement and approval.
- Agentic workflows
- An AI component can choose its next permitted step within an approved workflow, such as researching a record, preparing a message or routing a reply. Its permitted actions, escalation points and human hand-off remain explicit.
Inside one workflow, every time it runs
The same four steps behind every automated action we build, whichever of the five options we land on.
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An authorised source records something
An official market event, quote, account change, service date, message or evidence document enters the agreed workflow with its source and date retained.
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Rules verify and prepare the case
Identity, required fields, duplicates, exclusions and deterministic checks run before AI summarises context or drafts an action.
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Uncertainty and sensitive cases stop
Conflicts, low confidence, suppression, disputes and decisions needing commercial or professional judgement route to a named person.
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The person decides and the outcome is logged
In an approved sales or recovery sequence, the workflow can send the agreed routine message. People take over exceptions, corrections, pricing, negotiation and professional decisions, and the outcome is logged.
A worked example, in a trade quote book
A quote sent 24 days ago has no acceptance, loss reason or recorded follow-up. The account is active, but the record alone does not show why the customer has not responded.
The workflow checks the quote status, account owner, active opportunities and contact controls, then prepares or sends the agreed factual question. The salesperson takes over useful replies and owns every commercial decision.
Between us, from first call to go-live
The four steps above are what happens inside one workflow. This is what happens between us, from the first conversation to the point where somebody else is maintaining it.
Every engagement uses the same decision gates through contract. The paid entry stage then depends on the chosen product: there is no universal audit quietly inserted before a proposal. Nothing skips human review, and nothing goes live without an accountable owner and a tested fallback.
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Fit call
A short, no-cost call to understand what you run today, confirm your systems can realistically support what you want, and agree whether we're the right fit.
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Qualification
We confirm the segment, one repeated workflow, source records, accountable owner, native-stack options and disqualifying constraints before recommending paid work.
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Scoped proposal
You receive the named product, entry stage, price, evidence boundary, exclusions, success measures and human approval points in writing.
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Contract and access boundaries
Nothing paid starts until the scope is agreed, the contract is signed and the permitted accounts, data fields, retention and access route are recorded.
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Product-specific entry stage
Only then do we run the agreed campaign design, Revenue Recovery Data Audit, Operational Evidence-Flow Audit, front-desk Vendor Review or another explicitly scoped pilot.
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Build or configure
The agreed route is configured against your accounts and platform, with approval points designed in from the start rather than added afterwards.
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Testing and human review
Every workflow is tested against edge cases before go-live, and every escalation gate is checked to make sure it actually holds under real conditions.
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Handover and controlled go-live
Your team receives the playbook and training before a bounded go-live, with a named owner, fallback route and stabilisation period.
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Monitoring and review
Where continuing operation is part of the signed scope, a review cadence checks quality, exceptions, native-platform changes and whether the workflow should expand, change or stop.
The same method, made specific per offer
Each solution page names its buyer, sources, output, inference boundary and human decision.
What your team actually sees
- The original source and material fields behind each case
- Why the case appeared, which checks passed and what remains uncertain
- A prioritised action or evidence queue inside the existing operating workflow where practical
- A clear record of the human correction, decision, owner and outcome
Where AI fits, and where it never does
AI reads, matches, summarises and drafts where those capabilities help. In approved B2B sales and first-party recovery campaigns it can also send the agreed routine sequence; research and evidence workflows begin read-only or draft-only. Rules handle deterministic checks, while accountable people own exceptions and decisions.
AI handles
- Summarising source-linked context and preparing an agreed action
- Sending configured B2B or first-party recovery messages inside an approved campaign
- Matching records and surfacing missing or conflicting evidence for review
- Prioritising an agreed group of records using transparent client-defined criteria
- Routing low-confidence or sensitive cases instead of guessing
AI never handles
- Inventing sales intent, contact permission, urgency or a compliance conclusion
- Clinical, legal, regulatory, safety, credit, pricing or professional judgement
- Complaints, disputes or adverse decisions without a named human owner
- Replacing a reliable native workflow merely to introduce another AI component
Questions people ask before they've talked to us
Do we need to replace the system we already use? Usually not.
Usually not. We inspect native features, exports, APIs and existing task workflows first; enabling or configuring the current system can be the final recommendation.
Does a public record or old quote become an automatic lead? A public record becomes a usable B2B sales signal after the organisation, customer fit and business contact route are verified.
A public record becomes a usable B2B sales signal after the organisation, customer fit and business contact route are verified. Inside an approved campaign the workflow can then start the agreed conversation; an old first-party quote follows its separate recovery rules.
Can the first stage run without broad data access? That is the aim.
That is the aim. We choose one focused group of records and the minimum fields needed to test it, then expand only if the source quality and controls justify it.
Can we stop if the data or current software is not ready? Yes.
Yes. A valid assessment can recommend repairing the data, enabling the incumbent product or stopping rather than building.