The build side

AI systems that run inside operations

Five practice areas, fifteen services. Models that read, score and decide — wired into the systems you already run, with a person on every case the model is not sure about.

Automation

End-to-End Workflow Automation and Robotic Process Automation (RPA) – workflows that carry out the decision across systems never designed to talk to each other.

Artificial Intelligence & Machine Learning

Predictive Analytics, AI-Based Decision Systems and Intelligent Data Processing – models deployed against a measured baseline, not a demo.

Cloud Services

Cloud Architecture & Migration, Cloud Optimization and Secure Cloud Integration – sized so running costs stay inside a number you agreed to.

Technology Consulting

Solution Architecture, Legacy System Modernization and Tech Strategy & Roadmap – what to automate, in what order, and when a rules engine wins.

SME Enablement

Scalable Digital Tools for SMEs, Startup Acceleration Tech Stack and Budget-Friendly Implementation — applied AI sized for a team of twelve.

Human-in-the-loop delivery

Not a separate service: confidence thresholds you set, with everything below them routed to a named human queue with the source attached.
Measured, not estimated

Four numbers, every month

After go-live, the monthly report tracks the same four numbers – captured against a baseline measured before anything was deployed.

1

Straight-through rate

Share of work handled with no human touch.

2

Exception rate
How often work routes to your review queue.

3

Measured accuracy
Scored on a held-out sample of your own data.

4

Cost per decision
Inference and hosting, inside an agreed number.
From assessment to production

How an engagement runs

01

Assess

Two weeks. Baseline the process, label a sample of your own data, test candidate models and publish the accuracy. If it does not clear the bar, we tell you before anyone signs a build.

02

Architect

Solution design, data flow, threshold policy and a cost per document or decision, agreed before the first line of production code.

03

Build

Two-week increments, demoed live, evaluated against the held-out set every round so accuracy is never a surprise at the end.

04

Deploy

Shadow mode first, then progressive cutover with fallback to the existing process at every step.

05

Operate

Monitoring, drift checks, monthly accuracy reporting and a quarterly review of what to automate next.

Start with a two-week assessment on your own data