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Quickmation gives a small business the implementation team it would otherwise have to hire: specialist AI agents that do the work, an independent model that checks it, and a human engineer who signs off before anything reaches your systems.
No payment details. No sales call required.
Quote follow-up is being dropped
Finding 3 of 9 · Assessment report
Quotes are raised in your CRM and followed up by hand. Confirmed from your own answers during intake.
Follow-up depends on one person remembering, so it stops whenever that person is on site.
Around four to six hours a week of admin, projected from the quote volume you reported. Actuals will differ.
Automate the follow-up sequence, with a human checkpoint before any discount is offered.
Reviewed by a human engineer before it reached the client.
Why it usually stalls
None of these are model problems. They are all operating-model problems, which is why we built the platform around the process rather than around the model.
Something gets demoed, everyone is impressed, and then it dies at the point where it has to touch a real system with real money in it. Nobody was accountable for the last twenty percent.
It was built by a contractor who has moved on. It broke when a supplier changed a file format, and you found out because a customer called.
A model produced a number, the number went into a quote, and nobody could tell it was a guess because it was not labelled as one.
The operating model
This loop is the product. Work is produced by AI, verified by a different and deliberately stronger model, signed off by a human engineer, and approved by you before it reaches anything that matters. Then it is monitored, and the next opportunity comes from that monitoring rather than from a sales call.
Diagram of the seven-stage operating loop. Stages three and four sit in a human lane; the rest are performed by AI, and the sequence repeats.
Specialist agents write the automation, the integration or the software, working inside an isolated workspace against your real requirements.
A second, deliberately stronger model checks the output against the acceptance criteria. The model that reviews is never the model that built.
A human engineer reads the diff, the verification report and the risk level, then signs. Nothing reaches your systems without this signature.
Anything that touches money, customers or production waits for your explicit approval. High-risk changes need more than one approver.
Release runs through sandbox and staging first. Every deployment is reversible and recorded with who approved it and what exactly shipped.
Live automations are watched for failures, drift and cost. Incidents open themselves and are routed to an engineer.
Run telemetry feeds opportunity discovery, so the next improvement is proposed from evidence rather than from a sales calendar.
The AI workforce
Each role has a narrow job, its own instructions and its own model binding. A generalist that does everything is a generalist that cannot be held to an acceptance criterion.
Maps how work actually moves through your business today, including the steps nobody documented.
Reads your tools, your data and your market so recommendations are grounded in your situation, not a template.
Ranks opportunities by payoff, effort and risk — and says plainly which ones are not worth doing.
Designs the automation: triggers, branches, failure paths and the human checkpoints in between.
Writes and refactors the code, in a sandboxed workspace, against acceptance criteria agreed up front.
Connects the systems you already pay for. Credentials are scoped to a single run and revoked at the end of it.
Moves, cleans and models the data behind reporting and knowledge systems, with lineage kept intact.
Tests the build against the acceptance criteria and produces the verification report an engineer signs.
Scans for exposed secrets and personal data, and enforces the egress allowlist on every outbound call.
Writes the runbook, the SOP and the handover notes so your team can operate what was built without us.
Watches live automations, opens incidents on failure or drift, and escalates to a human on call.
Reads run history and proposes cheaper, faster or more reliable versions of what is already deployed.
Sequences the plan, chases the blockers and keeps the delivery dates you were given honest.
The model that reviews work is never the model that produced it. A reviewer at the same capability as the author cannot catch the author’s systematic mistakes.
The rule we will not bend
Every claim the platform renders — in a report, a plan, a dashboard or an email — carries one of four labels, on the claim itself. You should always be able to tell in one glance whether you are reading something we checked or something a model inferred.
The same rule applies to us. When the platform is running without a live model connection, every artifact it produces is stamped as simulated rather than quietly presented as real output.
Checked against source data or your own systems, and signed off by an engineer. This is the only label that means we stand behind it as true.
A model's reading of the evidence. Useful, frequently right, and not yet confirmed by a human. Treat it as a strong hypothesis.
A projection. Hours saved, cost, payback period. The method behind it is visible, and the actual number will differ.
A proposed action waiting on your decision. Nothing under this label has happened or will happen without approval.
Capabilities
You are not buying twelve products. The assessment decides which of these your first project needs, and the same team, ledger and approval process runs all of them.
A ranked, costed plan for where AI pays off — and where it doesn't.
Turning an approved plan into something running in production.
Named roles that handle inbound work, with escalation rules you set.
The repetitive path between your systems, built to fail safely.
Connecting the systems you already pay for, with scoped credentials.
Purpose-built tools for the process no off-the-shelf product fits.
Your documents, prices and policies made answerable — with citations.
Numbers you can act on, with the derivation shown.
A site that captures enquiries and feeds them into your systems.
Someone accountable when an automation breaks at 6am.
Teaching your team to use what was built — and where not to trust it.
The whole lifecycle, run for you, with a standing improvement loop.
Industries
The constraints differ more than the software does. A missed emergency call and a missed statutory notice fail in very different ways, and the automation has to know which one it is dealing with.
Start here
No payment details, no system access, and no obligation at the end of it.
Around fifteen minutes of structured questions about your tools, your team and the work that repeats. No integration or system access required to start.
Analysis runs over what you gave us and produces a set of candidate opportunities, each with its own estimated payoff, effort and risk.
A human engineer checks the analysis before you see it and removes anything that does not hold up. Findings arrive labelled as verified, analysis or estimate.
A ranked opportunity list, a recommended first project with a fixed scope and price, and the things we think you should not automate. Yours to keep either way.
The assessment takes about fifteen minutes of your time and returns a ranked, costed opportunity list reviewed by a human engineer. You keep the report either way.
No payment details. No sales call required.