We build enterprise software. Now it is AI-native.

The AI is in the architecture, not bolted on after launch. Cloud-agnostic by design, with deep AWS expertise where that is the fit. Not a research lab, and not a chatbot shop.

Book an assessment See what we build

The evidence, plainly

AWS Select Tier Services Partner Funding, credits and Bedrock depth where you run on AWS.

Argus, in production Our own multi-tenant platform, running Well-Architected and FinOps reviews against real AWS estates.

Agents, retrieval, guardrails, evals The parts of a production AI system, not the parts of a demo.

Partner-level validation of delivery capability, not a self-assessment.

Four people land on this page. Pick the one that is you.

01 You are choosing a delivery partner You need a firm that ships to production and hands it over running, not a pilot that stalls at the security review. See what we build 02 You are watching AWS spend A Well-Architected and FinOps review of your estate, read-only, with findings and cost posture in one report. See the review 03 You have been told to do AI Two weeks, fixed scope. You leave with prioritised use cases and a costed plan you can take to the board. Start with the assessment 04 You are an AWS partner Argus runs the same reviews across your customer accounts, multi-tenant, with no agents to install. See Argus

Most teams bolt AI onto the side. We put it in the load-bearing structure.

The difference does not show in a demo. It shows in month nine, when the thing is still standing.

Bolted on A model is called from a feature, behind a button someone added last quarter.

In the architecture Retrieval, agents and evals are part of the data and API design, so the system can change models without a rewrite.

Bolted on Quality is whatever the demo showed. Nobody can say if last week was worse.

In the architecture Evals run in CI against a real test set, so a regression fails the build rather than reaching a customer.

Bolted on Cost and access are discovered in production, usually by finance or by security.

In the architecture Guardrails, spend limits and audit trails are designed in, so the security review is a conversation and not a rebuild.

Five ways in. Every one ends in a system running in production.

01AI-native product engineering Full product and platform builds with AI in the architecture from day one, not bolted on after launch. 8-16 wks 02Generative AI and agents Systems that act on your company data and workflows, not generic chatbots. Where current enterprise demand is concentrated. 4-8 wks 03AI strategy and advisory Fixed-scope roadmap work, never open-ended hours. 1-3 wks 04Intelligent automation Workflow and document automation for operational pain. 3-6 wks 05Managed AI operations SLA-backed operation of deployed AI systems. Retainer

Fixed scope in, a running system out.

01 Assess Two weeks, fixed scope. Use cases found, prioritised and costed.

02 Scope One use case becomes a defined build with a date and a number attached.

03 Build Shipped to production on your stack, with evals and guardrails in place.

04 Operate Monitoring, tuning and cost governance under an SLA, if you want it.

Argus runs Well-Architected and FinOps reviews at partner scale.

Multi-tenant assessments of customer AWS accounts through CloudFormation-deployed, read-only cross-account roles. The same rigor we run for you, packaged for AWS partners.

Read-only by design, no agents to install Findings and cost posture in one report

See Argus

How access works

01 Customer deploys a CloudFormation stack In their own account, from a template they can read first.

02 It creates a read-only cross-account role No write permissions. No agents. Revocable by them, at any time.

03 Argus assumes the role and reports Well-Architected findings and cost posture, in one document.

This is the whole trust argument, and a security team can verify every line of it before granting anything.

Start with the AI readiness assessment.

We help enterprise teams find and ship AI use cases that pay for themselves in under 90 days, wherever your data and systems already run.

Two weeks, fixed scope. You leave with a costed plan you can act on, whether or not you build it with us.

What you leave with

A prioritised list of AI use cases, scored on value and effortA costed plan for the top one, with a delivery dateThe architecture it would need, on the stack you already runAn honest read on what is not worth doing yet Book an assessment No pricing games. We scope it on the call.