When you've hit the ceiling of what any SaaS product will let you do, this is the service. Custom AI software engineered to the exact shape of your operation — document intelligence, anomaly detection, predictive scoring, vision systems, decision automation, knowledge engineering.
45–60 min · Free · No pitch
There's a class of operational bottleneck that SaaS was never designed for. Your document taxonomy doesn't match any vendor's. Your anomaly pattern is specific to your industry. Your qualification logic has forty edge cases a generic prompt can't handle. Your decision criteria live in the heads of three senior people who are already stretched — and when they leave, the IP leaves with them.
Every week that problem sits unsolved, it compounds. In salaried hours, in errors, in missed revenue from decisions made without complete data. Bespoke AI development is the fix — engineered to the exact shape of your problem, and nothing else.
Categories of bespoke build we've engineered before. Yours might be one of these — or something none of these describes.
Parse, classify, summarise, route. Contracts, invoices, claims, case files, reports. Against your taxonomy, not a generic one.
Watches your operational data for the patterns that mean trouble — fraud, churn, fault, fail — and flags them early.
Trained on your real history. Scores leads, accounts, applications, risk, anything where prior outcomes can predict future ones.
Image and video understanding — QA, inspection, identification, counting, classification — for operations where humans currently look at pictures all day.
Rule-and-judgement engines that handle the routine decisions your senior people shouldn't be making. Escalates the edge cases.
Capturing what's in your senior people's heads into a system. So the IP stays even when the people don't.
Every build below started with a problem no off-the-shelf product was going to solve.
Bulk EPC portfolio audit against the national register. A hundred properties that took a working day to triage manually now process in minutes — using Jaccard string-similarity scoring and a live government dataset.
GPT-4 Vision reads post-job photos sent via Telegram; GPT-4 Turbo drafts the complete case study in ~60 seconds. Operator reviews in ~5 minutes. Cost per output: £0.10–£0.30. Infrastructure cost: £0/month.
Claude API generates personalised meal and workout plans factoring in NHS lab results (eGFR, HbA1c, cholesterol), Dexcom continuous glucose data, and insulin timing. A build generic fitness platforms cannot replicate.
The pattern that recurs most often inside the "custom AI" category.
Extract, classify, validate and route incoming documents — invoices, contracts, EPCs, claims, forms — into your CRM, accounting system, or case-management database without anyone retyping.
Higher-level service pages that custom AI builds commonly deliver against.
The pillar — wiring custom AI cleanly into your existing data, accounting, case-management, and industry systems.
When the custom AI build's job is to automate repetitive cognitive work — document review, classification, decision support — the workflow pillar is the framing.
Stacks of documents pile up — contracts, invoices, claims, reports — and your team reads each one by hand because no SaaS knows your exact taxonomy.
You have years of operational data, and nobody's mining it. The signals are in there. Nobody has time to look.
A skilled person makes the same judgement call thousands of times a year. The criteria are knowable, but they live in their head. When they leave, the IP leaves.
You've tried generic AI products. They cover 60% of the work and miss the 40% that actually moves the needle.
Documents come in, get parsed, classified, summarised, routed — automatically, against your real categories and your real rules.
Your operational data runs through a system that watches for anomalies, surfaces patterns, and flags opportunities your team doesn't have time to chase.
The judgement calls your senior people make get captured into a system that scaffolds the work, supports decisions, and preserves the IP when they're not on shift.
The exact 40% that generic AI products always miss — that's where the bespoke build pays for itself.
A generic AI product covers the 60% of work your operation shares with everyone else — and stops there.
A bespoke build engineers the 40% that's specific to your operation — the part SaaS was never going to know about.
Why it matters: The generic 60% is easy. The bespoke 40% is where the savings live.
Off-the-shelf classifiers train on the vendor's taxonomy and call your categories "miscellaneous".
A bespoke build trains on your taxonomy — your categories, your exceptions, your edge cases — and routes accordingly.
Why it matters: If a SaaS classification scheme had matched your operation, you'd be using it already.
A generic AI product locks you to the vendor's API, the vendor's pricing, the vendor's roadmap.
A bespoke build ships into your repository, runs on your infrastructure if you want, and changes as your operation does.
Why it matters: The build pays for itself once. Vendor licences keep paying.
In short: a custom AI build engineers a solution around your specific bottleneck when your problem doesn't fit a category — large-scale document classification against a proprietary taxonomy, pattern detection across years of internal data, bespoke decisioning in a specialist workflow. The generic AI model still sits underneath, but is wrapped in logic that knows your operation.
Still have a question? Book a discovery call — direct line to me, Dean.
A custom system that turns a hundred-property portfolio audit from a full day into minutes.
Read the case studyCASE STUDYA bespoke pipeline that takes post-job photos and a one-line description and ships published content at £0.30 per output.
Read the case studyGUIDEDiagnostic framework for finding the problems that justify a custom system.
Read the guideEvery week you're running a manual workaround for a problem a bespoke system would solve, you're paying for it — in salaried hours, in errors, in decisions made without complete data.
A 45–60 minute discovery call. Describe the bottleneck. I'll tell you whether a custom AI build is the right shape and what it would cost — and you walk away with that map regardless of outcome. The code is yours. No vendor lock-in.
Map my three bottlenecks