AI · Automation · FinTech Engineering
n8n, RAG and custom LLM systems, wired into your stack.
Tell us the problem and we will scope it — reply within one business day.
What we do
The shape of an engagement, what it runs on, and what it is held to once it is live.
What it is held to
25+ systems in production
Engineering that stays after the launch.
You ↔ the engineers
Three ways in. All three start with a written spec you keep either way.
What we build on
TypeScript Python Go Postgres Redis AWS GCP Docker Terraform GitHub Actions
100%
Every build instrumented from the first commit, so what you are told about performance is measured in production rather than estimated in a deck.
Yours from day one
Selected work
Five engagements, with the figures we measured on the way out. Every number here also appears on its own case-study page.
01
Enterprise support
Peak-hour call abandonment sat at 35%. A voice agent now answers in under two seconds, resolves most calls outright, and hands the rest over with context.
Read the case study
82%
Calls resolved without a human
1.8s
Response latency
4.5K+
Appointments booked monthly
02
Fintech
Bot attacks had climbed 400% while engagement fell 20%. Multimodal support now defends the funnel and answers in under half a second, with no extra step for real users.
Read the case study
99.2%
Bot defence accuracy
60%
Faster voice response
87%
Onboarding completion
03
Real estate
Leads were waiting four hours for a callback. Every enquiry is now engaged within ten seconds, booked into the calendar, and handled without a coordinator.
Read the case study
42%
More property tours booked
10s
Lead response time
5,500$+
Saved monthly
04
Global retail
Churn in non-English markets ran at 22%, driven by cultural misreads. Sentiment is now mapped per locale, so the cases that need a human reach one.
Read the case study
95%
Escalation detection precision
89%
Sentiment accuracy
18%
Churn reduction
05
B2B SaaS
Ten hours a week were going into manual Reddit prospecting. A multi-tenant scraper now watches 1,000+ subreddits hourly and scores what it finds.
Read the case study
75%
Infrastructure savings
5K+
Leads captured daily
2min
Time to onboard a user
In-house products
Not case studies — products we built, shipped and still operate. The same engineers who would work on yours.
Before you ask
What we build, how an engagement runs, and what you are committing to when you book the call.
Three things. AI agents, RAG systems and automation that take over work a team should not be doing. Trading and FinTech infrastructure — order and risk management, broker integrations, reconciliation and the dashboards around them. And AI-driven MVPs: a production-grade first version you can raise on and build past, rather than a prototype you throw away.
A scoping call, thirty minutes. You describe the problem and what it currently costs you; we come back with a written spec, a timeline and a fixed estimate. If we are the wrong people for it, we say so on that call rather than three weeks in.
Fixed-scope and fixed-price for defined builds, priced from a written spec you keep whether or not you go ahead with us. Ongoing work — systems already in production, including the on-call parts — runs as a monthly retainer instead. We will tell you which one your problem is.
It gets re-specified, and you see the new price and the new date before anything is built against it. What we hold to is that the schedule is agreed at week one and a change to it is a decision you make, not a surprise you are handed at the end.
You do — the repository, the infrastructure definitions and the documentation, from the first commit rather than on final payment. Nothing we build depends on a THWorks account or licence to keep running, and handover is part of the work rather than an extra.
Yes — we work with founders and teams around the world, and engagements run remotely by default. We agree a fixed overlap window up front, so timezones are a schedule rather than a scramble.
Next step
A scoping call is 30 minutes and gets you a fixed estimate. If we are the wrong people for it, we will say so.
