AI Integration
AI integration puts working intelligence inside the product and workflows you already run — document extraction, classification, natural-language search, assistants — scoped as product features with usage metrics attached. Our anchor proof: a multi-system pipeline with LLM classification that cut a team’s manual data entry by roughly 80% in its first month.
The free 30-minute session maps the data flows in your product and operation and marks where AI pays first. You keep the marked-up map either way.
Four integration moments we keep meeting
The roadmap has an AI line nobody has scoped
“Add AI” sits in the plan like weather. Meanwhile competitors ship specific features: extraction here, summarisation there, search that understands questions. The unlock is treating AI as product features with owners and metrics — which is a scoping exercise, not a research project.
Documents arrive, humans re-type them
Invoices, orders, applications, reports — structured information delivered as PDFs and email, converted to system records by paid attention. Extraction plus validation is the most reliably profitable AI integration there is, and the one we have the hardest numbers for.
Your product’s search does not understand questions
Users ask questions; your search matches keywords. The gap sends them to support, or away. Semantic search and assistants grounded in your content close it — measurably, in deflected tickets and completed tasks.
The first integration attempt is stuck at 80% accuracy
A model that is right four times in five is an unshippable demo without the engineering around it: confidence thresholds, human-review queues for the uncertain fifth, feedback loops that improve the split. That wrapper is usually the missing piece, and it is our home ground.
Every operations team has a re-typing layer nobody chose to build. Extraction plus validation is how it quietly disappears.
What we integrate
Document and data extraction
PDFs, emails and scans into validated system records, with confidence scoring and a human-review queue for the uncertain cases — the pattern behind the −80% number.
You get: an extraction pipeline with accuracy you can see, wired into your systems.
Classification and routing
Tickets, leads, transactions and content sorted and routed by meaning, with every decision logged and reversible.
You get: routing that scales with volume instead of headcount.
Assistants and semantic search in your product
Answers grounded in your content, with citations, refusal behaviour for out-of-scope questions, and usage analytics.
You get: a product feature with adoption numbers, not a bolted-on chat box.
The engineering wrapper that makes models shippable
Thresholds, review queues, fallbacks, cost controls, evaluation sets and drift monitoring — the difference between a promising model and a dependable feature.
You get: production discipline around every model call, documented.
How it works
Map and rank
The free audit plus a short scoping pass: candidate integrations ranked by value, feasibility and blast radius, each with a metric.
Build the first feature
Developed against your real data with the wrapper from day one — thresholds, review queue, logging. Shipped behind a flag.
Measure and harden
The metric is read honestly: accuracy, adoption, hours saved. Thresholds tuned, edge cases fed back into the evaluation set.
Extend across the roadmap
The wrapper and patterns are reusable — the second and third integrations land faster than the first.
Features with metrics, never magic.
Proof, not claims
Integration proof is operational numbers, and ours come from systems still running.
LLM features beside systems of record
Secure non-AI database integrations under an LLM front end — the model reads and drafts; deterministic code writes. The integration pattern most regulated buyers need.
AI wired into telephony and CRM
An integration story as much as an AI one: speech, reasoning and your existing systems in one loop, with every action logged.
The anchor integration cut manual data entry by roughly 80% in its first month — 300+ records a week, handled by a pipeline instead of a person.
An integration is a system someone runs for years. Ours arrive with thresholds, queues and dashboards — the operating manual included.
Start with the highest-paying integration
One 30-minute session maps your data flows and ranks the candidate integrations by payback. The first feature is scoped in writing, priced before anything starts, and shipped behind a flag with its metric attached.
The canonical first move: we find where documents, tickets or records are handled by hand, and put a number on automating it. You keep the map either way.
Clients also buy
AI Strategy Consulting
A fixed-price consulting engagement, payable online: from "we should use AI" to a costed, buildable plan your board can approve.
AI Business Process Automation
From Zapier/Make/n8n hacks to production automation — pipelines that survive volume, audits and staff turnover.
API Integrations
Payments, CRM, fulfilment — anything with an API, wired in so it stays wired in. Stripe, PayPal, ChargeBee, Clover.
Every integration starts with a written plan: the metric, the boundary, the fallback. Then we build.
The questions buyers actually ask
Which AI integration pays back first?
In most operations: document and data extraction — invoices, orders, applications converted to validated records. Volumes are known, the human baseline is measurable, and the pattern is proven; our anchor case cut manual entry by roughly 80% in its first month.
Our data is sensitive — does it end up in a third-party model?
Only what the written boundary allows. We design integrations so the model sees the minimum needed — masked, scoped, or on infrastructure you control — and deterministic code performs the writes. The boundary is documented before development starts.
What if the model is only 80% accurate on our data?
That is the expected starting point, and the wrapper is the answer: confidence thresholds route the uncertain cases to a human-review queue, the review feeds the evaluation set, and the split improves. You get 100% handled — by the model where it is sure, by people where it is not.
How long does the first integration take?
Typically four to six weeks to a feature behind a flag, then two weeks of honest measurement and hardening. The scoping session gives you a written schedule for your specific systems first.
Do you work inside our existing stack?
Yes — your repos, your cloud, your review process where you prefer. Integrations ride over the APIs of the systems you already run; nothing needs replacing to start.
What does it cost to run afterwards?
Model usage is metered and we model it against your volumes during scoping — cost per document or per action, next to the human baseline. Cost controls and caps are part of the wrapper, not an afterthought.
What AI can actually carry in your business — the one-page version
The nine AI & Agents services on one printable page: what each one is, the situation it answers, and the first engagement that proves it. Built to be forwarded to whoever holds the budget.
AI in your product this quarter
Book the free 30-minute session and we map your data flows and rank the integrations by payback — or write two sentences about the work your team re-types today, and an engineer replies in one business day.
- 30 minutes, an engineer on the call
- You keep the written notes either way
- Nobody follows up more than once
