Solutions

Unify the data.
Compress the decision.

Turn fragmented enterprise data into one governed workflow, approved by your operators and measured against a business KPI.

The engagements

Assess. Plan. Build. Operate.

Each engagement builds on the last: establish readiness, define a fundable roadmap, deploy the system, then monitor performance in production. Every stage ends with a concrete deliverable and decision gate. Pricing is scoped to your environment.

Step 01 · Assess

AI Readiness Assessment

Every engagement starts here. A teardown of the enterprise systems maps what each one does, how they talk to each other, and the maturity of the data underneath. The result is a board-ready read on what is AI-ready today, what has to be fixed first, and the shortest path to a production use case.

Systems mapped by function
Integration coverage
Data maturity
Board-ready report

3 to 4 weeks · Pricing on request Enquire

Step 02 · Strategy

AI Strategy & Roadmap

The assessment names the gaps; the strategy sequences the moves. Opportunities are sized and ordered by return and readiness, delivered as a roadmap a board can actually fund, with the business case attached, not a deck of ambitions.

Opportunities sized and sequenced
Business case attached
12 to 24 month roadmap
Salesforce track available

8 to 12 weeks · Pricing on request Enquire

Step 03 · Build

AI Build & Implementation

The roadmap turns into working software. ASI builds what the environment needs, the data layer and the systems around it, then implements the use cases on top. This is the stage that unlocks the most value: not a proof of concept in a sandbox, a production system the team runs every day.

Software built to the roadmap
Use cases shipped on top
Guardrails and operator sign-off
Measured against the day-one number

12 to 26 weeks · Pricing on request Enquire

Step 04 · Monitor

AI Monitoring & Optimisation

Production is the start line, not the finish. A standing retainer keeps the live system under watch: agent performance, data drift, and the lift the build promised, with the next improvements queued and shipped on a set cadence. The decision layer compounds instead of decaying.

Agent performance under watch
Data and model drift checks
Monthly readout on the lift
Improvements queued and shipped

Ongoing retainer · Pricing on request Enquire

What it does

Six Capabilities, One Governed System.

Your workflow gets one trusted data foundation, an approval-controlled AI layer, and a clear measure of business impact.

Unify the data

Fragmented enterprise data is unified into one governed source of truth for the workflow.

Compress the decision

AI prepares the recommendation, supporting evidence, and expected impact—reducing decision cycles from weeks to days.

Ship agent workflows

Recommendation, approval, and execution happen inside the systems your team already uses.

Stay in control

Consequential actions require operator approval, with clear limits and rollback paths where supported.

Measure the lift

Every recommendation, approval, and action is recorded, creating an audit trail from source data to measured result.

Scale across the org

Add new workflows on the same governed data foundation instead of rebuilding the infrastructure each time.

How it goes in

Discover. Build. Deploy.

The required data is governed first. AI is then deployed into one production workflow with approval and measurement built in.

Phase 00 Days 01 to 30

Discover

Map the workflow, define the architecture, and select the operational decision to improve. The implementation price is fixed once scope and value are clear.

Workflow mapped · Price fixed

Phase 01 Days 31 to 75

Build the Data Layer

Fragmented enterprise data is unified into a governed warehouse or lakehouse—the foundation for production AI.

Data unified · Foundation built

Phase 02 Days 76 to 90

Deploy the Workflow

Governed agents are deployed into the workflow, with operator approval, auditability, and measurement built in.

Workflow live · Lift measured

See the 90-day pilot

Where it fits

One Operating Model, Adapted by Sector.

The systems and controls vary by industry. The pattern remains consistent: unify the required data, govern the workflow, and measure the result.

01 Financial services Risk, exposure, and reporting data unified so the regulator-driven call lands on time, not after the deadline.

02 Manufacturing Cost, production, and supply data pulled into one state through the ERP migration, so the decision survives the move.

03 Insurance Claims and policy data stitched across the systems that don't talk, so the loss-to-pricing call is made in days, not weeks.

04 Retail and ecommerce Inventory, channel, and demand data unified across every channel, so the pricing and stock call lands on one read.

05 Professional services Time, cost, and margin data brought into one view, so the call on what's actually profitable gets made now.

06 And beyond The compression pattern is sector-agnostic. Wherever a recurring decision is bottlenecked by fragmented data, the layer fits.

Start with One High-Value Workflow.

Identify the recurring workflow, the systems behind it, and the KPI it should improve. ASI will scope the shortest path to production.

Book a 90-day pilot

Unify the data.Compress the decision.