AI Integration
We Put AI to Work Inside the Tools You Already Use.
One workflow at a time, built into your CRM, support desk, accounting, and internal apps. Tested on your real cases before it ships, and measured on hours saved and revenue.
Available for new projects
Who This Is For
If one of these is you, keep reading.
Businesses With a Lot of Admin
Invoicing, intake, scheduling, and paperwork that someone does by hand today.
Companies With a Stalled Pilot
A ChatGPT or Copilot experiment that impressed everyone and changed nothing on the P&L.
Product Teams Adding AI
A feature that has to work on customer data, for every customer, without embarrassing the company.
Support and Customer Service Teams
Fewer tickets reaching a person and faster answers, without the quality dropping.
Leaders With Staff Using AI on the Side
An approved version, so company data stops going into personal accounts.
Why Bring In AI
Six reasons the conversation starts.
Pilots That Never Ship
MIT's 2025 study found 95% of organisations got no measurable return from their generative AI spend. The models work. Getting them into day-to-day use is the hard part.
Margin Pressure
The biggest savings in that same study came from back-office automation, mostly by cutting what companies pay outside firms to do.
Customer Expectations
Faster answers, service that feels personal, an instant update on where things stand. Small businesses say AI is now what it takes to keep up.
Competitive Pressure
Two-thirds of small-business owners say adopting AI is essential to stay competitive. The answer has to be a specific job.
Board Visibility
Half of generative AI budgets go to sales and marketing because the results are easy to see. The back office is where the money is.
Staff Using AI on Their Own
People are already pasting company data into personal AI accounts. Leadership wants an approved version with proper access controls.
What Makes AI Hard
Calling the model is the easy bit.
Pilot to Production
S&P Global found 42% of organisations abandoned most of their AI projects in 2025, up from 17% the year before.
Data That Isn't Ready
Gartner expects 60% of AI projects without AI-ready data to be dropped. A messy CRM gives you a messy agent.
Getting It Wrong
McKinsey found half of organisations had something go wrong with AI, and the most common problem was inaccuracy. An agent that's confidently wrong is worse than no agent.
Cost Once It's Live
Running the model every day is where most of the lifetime cost sits. Most budgets are set from the demo, and production costs a lot more.
Security and Permissions
Prompt injection is number one on OWASP's list of risks for AI apps. Give an agent too much access and one bad input can take the lot.
Getting People to Use It
Kyndryl found 45% of CEOs say most of their staff push back on AI. A tool nobody uses saves nothing.
AI in Numbers
Why we measure before and after.
95%
of organisations saw no measurable return on generative AI investment.
2×
the deployment rate for AI built with external partners versus internal builds.
42%
of organisations abandoned most of their AI initiatives in 2025, up from 17%.
40%+
of agentic AI projects predicted to be cancelled by the end of 2027.
Sources: MIT NANDA, The GenAI Divide (2025) · MIT NANDA, The GenAI Divide (2025) · S&P Global Market Intelligence, via CIO Dive (2025) · Gartner (June 2025)
Where AI Projects Go Wrong
Five failures with names attached, and how we build to avoid them.
A Tool That Sits Outside the Work
If it doesn't live where people do their jobs and doesn't learn from their corrections, it gets dropped. MIT found in-house builds made it to production a third of the time. Builds with an outside partner, two-thirds.
The Wrong Problem
RAND's list of reasons AI projects fail starts with people expecting the wrong thing, then bad data, then chasing the technology instead of the result.
A Chatbot You're on the Hook For
Air Canada was held liable when its chatbot gave a customer wrong fare advice. The tribunal didn't accept that the bot was somehow separate from the airline.
Cutting Cost Before Checking Quality
Klarna replaced hundreds of support staff with AI, then hired people back. The CEO said cost had been too big a factor in the decision.
Letting It Change Things With No Guardrails
An autonomous coding agent deleted a live database during a code freeze, then gave a misleading account of what it had done. How much damage an agent can do is a design choice.
How We Build It
One job at a time, built into your systems, measured once it's live.
One Workflow, One Number
We pick one narrow process, usually in the back office, and agree the number before we touch a model. Hours saved, tickets handled without a person, days to get paid.
Data and Access Check
Where the data lives, how clean it is, who's allowed to see what. If it isn't ready, we fix that first or tell you the idea isn't workable yet.
Test Cases Before We Build
A set of real examples from your own data with known right answers. The agent has to pass them before it ships and keep passing as we change things.
Only the Access It Needs
It reads by default. It writes only where you've said it can, and every write is logged. Test and live kept apart.
A Person Checks the Risky Bits
Anything with real consequences gets looked at by someone before it goes out. McKinsey found the companies doing best with AI build in these checks more often than the rest.
Built into Your Tools
CRM, support desk, accounting, your internal apps. The agent works where the work already happens.
What we build with
- Claude API
- OpenAI API
- Vercel AI SDK
- LangChain
- PostgreSQL + pgvector
- Pinecone
- Python
- TypeScript
- n8n
- AWS
Built the same way: Receivables · Banyan · Egis · Lift
Why We Ship One Workflow at a Time
A six-month AI programme with one launch at the end is how 95% of companies get no return. We ship one workflow every two weeks, tested on your cases, and measured from the day it's live.
A Prototype on Your Real Cases in Weeks
Inside the first month the model is running against examples from your own data with known right answers. You see the score.
Real Results Steer the Roadmap
Accuracy, cost per task, and whether people use it are on a dashboard from day one. Those numbers decide which workflow comes next. Workflows that don't move a number don't get built.
Stop at Any Point
Every two weeks you see a live demo of what's running and make a decision: keep going, change direction, or stop. You are billed per step, so if you stop, you pay for what shipped.
Change Your Mind Cheaply
Swapping the model, the prompt, or the workflow costs one step. The two things we fix early are the number we're measuring and what the AI is allowed to see and change.
Riskiest Assumption First
Is the data clean enough, and can the model handle your hardest cases? Both get tested before anything is wired into your systems. If the use case is wrong, you find out in week two for the price of a prototype.
No Big-Bang Rollout
A person reviews what the AI produces before it goes out. It earns more freedom as the test scores and the team say it has. Nothing gets full autonomy on launch day.
How a Fixed-Price AI Build Runs
Short loops. A working prototype against your real cases in weeks, then built into your tools one workflow at a time. You own the code throughout.
- Typical timeline
- 4-12 weeks
- Prototype on real cases
- Week 2
- Live demo
- Every 2 weeks
- 01
Pick the Use Case
Week 1A fixed-fee week. We rank the candidate jobs by what they're worth, how ready the data is, and how much can go wrong. Then we agree the number we'll measure and start with one. You keep the ranking whether or not you go ahead.
- Ranked use cases
- The number we'll measure
- Fixed price for the build
- 02
Check the Data and Systems
Week 1-2How we get at the data, how clean it is, who's allowed to see it. Gaps get fixed or the scope gets trimmed.
- Data readiness report
- Access plan
- 03
Prototype Against Real Cases
Week 2-3Test cases built from your own examples. The prototype has to pass them before we build any further.
- Test cases
- Passing prototype
- 04
Build It into Your Systems
Week 3-8Connected to your tools with the minimum access, a log of everything it does, and cost controls like cheaper models where they'll do and caching repeat answers. A new workflow every two weeks.
- First workflow live, week 4
- New workflow every 2 weeks
- Live demo every 2 weeks
- 05
Roll Out With a Person in the Loop
Week 6-10Someone reviews what it produces first. It gets more freedom as the tests and the team say it's earned it.
- Review queue
- Audit log
- 06
Ship, Measure, Improve
OngoingWe track how often it's right, what each task costs, and whether people use it. Corrections go back in so it gets better. On a monthly retainer, or handed to your team with the test suite.
What It Costs
Fixed fee
Ends with a scope document and a fixed price for the build. Yours whether or not you go ahead.
- Scope document
- Fixed price
$12k - $80k
The number moves with how clean your data is, how many systems the AI has to connect to, and whether a person needs to review its output before it goes out.
- Billed
- Per 2-week step
- Stop
- At any point
- Price moves
- Only if scope does
Build, Buy, or API
We'll recommend whichever fits, including the cheap one.
Buy an Off-the-Shelf Tool
The right call when a vendor's product already fits the job and your data. You give up some control and you're tied to them. Often the right first step.
Plug a Model In via API
The right call for most custom work: your workflow, your data, your permissions, a hosted model behind it. Most of our AI work sits here.
Train or Fine-Tune Your Own
Only worth it when you have a large, specific dataset the general models can't handle. Rarer than the vendors make out.
Common Questions
If yours isn’t here, book a call and ask.
Got a Pilot That Never Shipped?
Tell us what it was meant to do and where it got stuck, on the call. If it's a fit, a one-week discovery follows, and at the end of it you know whether it's workable, what to measure it on, and a fixed price to get it live.