Taking new engagements for Q4
AI that survives
contact with
your business.
Anyone can demo a chatbot. We build the version that runs on a Tuesday afternoon in your helpdesk, against your data, inside your rules - and we stay to keep it working.
- Fixed-fee diagnosis, roadmap is yours
- Production in weeks, not quarters
- Full handover - no lock-in
4
phases, each ending in a decision point
you can stop at any one of them
<4 wks
from kickoff to one workflow running in production
single-workflow scope, not a platform
100%
of the diagnose roadmap is yours to keep
whether or not you build with us
0
client outcomes published that we cannot attribute
when we can name the engagement, the number goes here
Works inside the stack you already run
Works inside the stack you already run: Salesforce, HubSpot, Zendesk, Intercom, Shopify, Slack, Microsoft 365, Google Workspace, Snowflake, Klaviyo, Stripe, Twilio, NetSuite, Notion, Zapier, AWS, Azure, Postgres.
The problem
Most AI projects die in the gap between demo and Tuesday.
The pilot works. Then it meets a real customer, a real edge case, a real permission boundary - and quietly stops being used. That gap is an engineering problem, not an ambition problem.
How we close it- 01
Nobody named the number
A project without a target metric cannot succeed or fail - it just continues. We fix the number first: resolution rate, cost per order, hours returned. Everything else follows from it.
- 02
It was never actually integrated
A model that cannot read your order history or write to your CRM is a demo with extra steps. Real value starts at the point the system is permitted to do something.
- 03
Quality was never measured
Without an evaluation set built from your own cases, "it seems good" is the only signal available - and it degrades silently as models, products and questions change.
- 04
There was no path back to a human
Systems that cannot escalate produce the worst outcome available: a confident wrong answer to a customer who now has to start again.
Capabilities
Seventeen practices.
One delivery model.
Start anywhere. Every engagement runs through the same four phases, so a support agent and a forecasting model are held to the same standard of evidence.
- 01 - Support
AI Customer Support Agents
Most support bots are a filter your customers learn to defeat.
Read more - 02 - Email
AI Email Automation
Batch sends and five static templates leave most of the value on the table.
Read more - 03 - Revenue
Lead Generation & Management
Most pipeline is not lost to competitors.
Read more - 04 - Operations
Business Process Automation
The expensive work is rarely inside a system.
Read more - 05 - Commerce
AI for E-Commerce
Online retail runs on decisions made thousands of times a day: what to show this visitor, what to reorder, what to price, what to flag.
Read more - 06 - Engineering
Custom AI Development
Some problems are specific to you: your terminology, your rules, your data, your edge cases.
Read more
How we work
Diagnose. Prove.
Integrate. Operate.
- 01
Diagnose
Days 1-2We sit with the people doing the work and map where time actually goes - queue by queue, handoff by handoff. You get a ranked list of automatable workflows with an effort and payback estimate against each one.
Opportunity map + costed roadmap
- 02
Prove
Week 1We build the highest-value workflow first and run it against your real historical data, offline. You see accuracy, escalation rate and cost per task before anything touches a live customer.
Working prototype + evaluation report
- 03
Integrate
Weeks 2-3The system connects to your CRM, helpdesk, store and data warehouse through their supported APIs. Permissions, audit logging, PII handling and human-escalation paths are built in, not bolted on.
Production deployment + runbook
- 04
Operate
OngoingModels drift and your business changes. We monitor quality continuously, re-run evaluations on every change, and tune against the metrics you actually care about - not benchmark scores.
Monitoring, evals, quarterly review
FAQ
Questions we get asked first
Something not covered here? Ask directly - we answer questions before contracts.
Ask usHow long before we see something working?
A single well-scoped workflow typically runs in production within three weeks, with a testable prototype inside the first. Broader programmes are sequenced so something ships every few weeks rather than in one large release.
Do we need clean data or a data team first?
No. Most of our engagements begin with the data as it actually is - messy exports, half-filled CRM fields, PDFs. Part of the diagnose phase is establishing what is usable today and what genuinely needs fixing first, rather than a year-long data project ahead of any value.
Which models do you use?
We stay model-agnostic and select per workload against cost, latency and accuracy - frontier hosted models where reasoning quality matters, smaller or open-weight models where volume and privacy dominate. The architecture keeps that choice swappable, so a better or cheaper model later is a configuration change.
What happens to our team?
In practice, the routine tier of work shrinks and the complex tier grows. We plan the role changes with you during the diagnose phase and include enablement so your team can adjust prompts, review escalations and read the dashboards without us.
How do you handle accuracy and hallucination?
Systems are grounded in your own content with retrieval, constrained to the actions they are permitted to take, and measured against a held-out evaluation set that we build from your real cases. Confidence thresholds route uncertain cases to a human, and every change is re-evaluated before it ships.
What does it cost?
The diagnose phase is a fixed fee and delivers a costed roadmap you own regardless of whether you continue. Build work is quoted per workflow, and ongoing support is a monthly plan sized to the number of systems in production. We will give you a range on the first call.
Free calculators
Answer it yourself before you call anyone
Three calculators for the arithmetic behind an AI decision. Each shows its working, and each is willing to tell you not to build the thing. No sign-up, and nothing you enter leaves your browser.
Automation payback
Hours returned, annual value and payback in months - checked at a conservative automation rate as well as your own.
How many test cases do we actually need?Eval set size
Margin of error at 95% confidence, sized from per-category counts, and the point where more cases stop buying precision.
Where should the confidence gate sit?Escalation threshold
Compare two gate settings on your own measured numbers: deflection, errors reaching customers, and total weekly cost.
Let's talk
Bring us the workflow that hurts.
Thirty minutes, no deck, no obligation. We will tell you what we would build, roughly what it costs, and whether it is worth doing at all.