Solutions

Unify the data.
Compress the decision.

Fragmented systems in. One approved decision out, in days not weeks. ASI is the decision layer for the mid-market enterprise.

The engagements

Assess. Strategy. Build. Monitor.

Not a menu, a ladder. Every engagement starts with the readiness assessment, and each stage earns the next: the read on what is AI-ready, the roadmap a board can fund, the build that ships it, and the watch that keeps it honest. Each ends in something real, not a slide deck. Pricing is scoped to the environment and shared on request.

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 decision layer.

First the data scattered across the business gets pulled into one place the agents can read. A decision that used to take weeks of chasing numbers between systems now takes days. Nothing runs until an operator approves it.

Unify the data

Fragmented enterprise data, resolved into one trusted decision layer. Identity stitched across every system, versioned and scoped.

Compress the decision

The calls that take weeks get drafted in hours. The agent reads the state, sizes the move, and surfaces the recommendation with the math attached.

Ship agent workflows

Recommendation, approval, and execution on one surface. The work finishes in the enterprise stack, not as a slide in a deck nobody implements.

Stay in control

Every move is proposed, not taken. Operators approve consequential calls and hold the brake, and the agent rolls back where the primitive allows.

Measure the lift

Every recommendation, approval, and rollback is recorded. The audit spine runs through every action, so the number moved stands up to scrutiny.

Scale across the org

Name the next workflow and the substrate carries it. One ontology, many agents, the same legible state the team already trusts.

How it goes in

Discover. Build the data layer. Ship the agents.

The data layer gets built first. Agents go on top. The whole thing ships into production, not into a strategy deck.

Phase 00 Days 01 to 30

Discover

Discovery maps the workflows with use-case and sequence diagrams and ERDs, delivers the architecture docs and a PRD, and names the call to move. Price is set here, once the scope is known.

Architecture · PRD · Price set

Phase 01 Days 31 to 75

Build the data layer

Fragmented enterprise data is unified into one layer, a warehouse or lakehouse. Pure infrastructure, no AI yet. The foundation everything else runs on.

Warehouse or lakehouse · Foundation built

Phase 02 Days 76 to 90

Ship the agents

Agentic AI layers onto the clean foundation, the workflow ships into the enterprise stack, and the lift is measured against the number named at the start. Every move stays under operator approval, audit trail by construction.

Agents shipped · Lift measured

See the 90-day pilot

Where it fits

Same job, every sector.

The layer is sector-agnostic. A bottlenecked decision is a bottlenecked decision, whatever the industry on the door.

01 Property and real estate Deal, valuation, and portfolio calls compressed from weeks of scattered files to a single read. The anchor sector and proof point.

02 Finance Unify exposure and pipeline data so the decision that drives the number lands the same day it matters.

03 Construction Pull cost, programme, and risk into one state the agent can read, so the call that holds the schedule gets made on time.

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

Name the decision. ASI compresses it.

Name the workflow and the call that takes too long. A scoped readiness assessment proposal lands in forty-eight hours if the fit is there.

Book a 90-day pilot

Unify the data.Compress the decision.