Applied AI for real operations. One accountable partner.
Think. Build. Prove.
An AI-powered technology partner. We understand the problem, build the system around it, and prove the result against a number you chose.
Describe the problem in a sentence. It lands in our inbox, not a chatbot.
- Finance & back office
- Retail & e-commerce
- Logistics
- Manufacturing
- Legal
- SaaS
- Healthcare
- Education
- Hospitality
- Professional services
What we take on
Six disciplines under one roof, so a problem is never handed between vendors. Pick the one that fits, or bring us the problem and we will tell you which it is.
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AI integration
Copilots, document intelligence and agents wired into the systems you already run, measured against numbers you already track.
- Custom copilots and chat agents on your own data
- Document intelligence and OCR pipelines
- Retrieval, ranking and evaluation for LLM systems
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Process automation
Back-office and workflow automation that removes the manual steps, with people kept in the loop where it counts.
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Software engineering
Web, mobile and API products built around how your operation actually works, not around a template.
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Cloud & infrastructure
Azure and AWS platforms engineered for scale, security and cost discipline.
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Data & intelligence
Pipelines, warehouses, dashboards and forecasts that turn operational noise into decisions.
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Technology advisory
Independent help framing the problem, choosing the architecture and setting the roadmap before anyone writes code.
75+
projects delivered
50+
clients served
10+
engineers
3+
years in operation
Why businesses bring us the hard ones
We began as a software studio. We became a technology partner because clients kept returning with the same gap: between the systems they owned and the outcomes they needed. Closing that gap is now the whole job.
One accountable partner
Software, automation, AI, cloud and data under the same brief, so nothing is lost between vendors.
A number, not a promise
Every engagement carries a measurable target agreed before we start, and we report against it.
Yours to keep
Code, infrastructure and documentation are handed over with every release. No lock-in.
Problems we have been handed
Each engagement starts with a problem statement and ends with a measured outcome. Here are three.
Finance & back office
Automated invoice processing for a distribution firm
Payment cycle cut from 12 days to 3. Data-entry staff redeployed to vendor management.
12 → 3
days to pay
3,000
invoices a month, automated
Retail
Support copilot for a multi-channel retailer
First-response time under 20 minutes across all channels. Agents moved from triage to relationship recovery.
SaaS
Churn prediction for a B2B software platform
At-risk accounts reached an average of 34 days before renewal. Churn on targeted accounts fell measurably within two quarters.
In their words
What the people who signed off on the work say afterwards.
They started by asking what number we wanted to move. Six months later that number had moved, and the system was ours to keep.
The first conversation saved us from buying a platform we did not need. What they built instead was smaller, faster and paid for itself in a quarter.
Our field engineers actually use the app, which is the only review that matters. Dispatch finally sees the day as it happens.
- Azure
- AWS
- .NET
- React
- Next.js
- Python
- OpenAI
- Azure AI
- PostgreSQL
- SQL Server
- Databricks
- Power BI
- Kubernetes
- Terraform
- React Native
- .NET MAUI
How we work
Four stages, in that order, every time, each ending with a number rather than an opinion.
01
Listen
Week one
We sit with the people doing the work, and measure the baseline before anything changes.
02
Design
Week one to two
One page: the problem, the approach, and the number that will prove it worked.
03
Build
Two-week increments
Short, reviewable increments with the measurement wired in from the first release.
04
Prove
At every checkpoint
The number reported against the baseline, whether or not it flatters us.
Bring us the problem.
A first conversation is free and usually enough to tell you whether this is a software, automation or AI problem, and what it would take to solve.