MAS-AI
Bring us a bottleneck

What we build, and how we build it.

Three kinds of work, two practices that run through all of them, and two depth areas for specific questions.

What we build depends on the gap.

One method, different answers. Pick one to see how the same operation changes.

A model where it earns its place: answers from your own documents, drafts a person approves, decisions with a trail.

OpenAI, Anthropic, Gemini, Grok or open-weight models · retrieval · document extraction and OCR · evaluation

All services

How we build

Two practices run through every project. Each also stands on its own when that is what you need.

  • Private, local and cloud AI

    Run AI on your devices, your servers, a private cloud or the public cloud, chosen per project.

    • On-device deployments
    • On-premises servers
    • Private cloud

  • Security, evaluation and governance

    Security reviews, evaluation, audit trails and human approval where an action matters.

    • Security reviews of apps and AI features
    • Evaluation against real examples
    • Audit trails and traceability

Where buyers arrive with a specific question

Two areas with their own questions, their own constraints and their own pages.

  • Knowledge systems and RAG

    Answers grounded in your own documents, with citations and a refusal when the source is missing.

    Read the page

  • EU AI Act readiness

    Classification, documentation, logging and human oversight for AI systems sold into the EU.

    Read the page

How we deliver

What a production AI system needs beyond the model. We plan each of these with you before the build starts.

  • Workflow and data first

    We start with the work: who does it, what a good result looks like, and whether the data behind it is complete, current and permissioned. If the data is not ready, we say so and scope that first.

  • Existing systems stay

    We connect your current and older systems through a thin adapter instead of rewriting them, with rate limits and health checks. When a system is down, work queues visibly instead of guessing.

  • Identity and permissions

    AI parts run under their own identity with only the access a task needs, and respect each user’s existing permissions through your sign-on and roles. We review access with your IT team.

  • People approve

    We agree which actions run on their own, which need a named approver, and who a case escalates to if nobody responds. Every approval is logged.

  • Tested, monitored, auditable

    A test set agreed with you before go-live and rerun on every change, traces and logs of what the system did, and failure paths tested on purpose.

  • Secrets and boundaries

    Credentials in a managed secrets store, network access limited to what each part needs, and only the data a task needs sent to a model. Provider terms on training, retention and location are checked against your requirements.

  • Speed, quality and cost

    Latency, quality and running-cost targets set up front and tested on your workload. We pick the smallest model that meets the bar and show you the running cost before we build.

  • Staged rollout and rollback

    Releases start with a small group or alongside the current process. Prompts, models and data indexes are versioned together, and rollback is rehearsed, not assumed.

  • Training and handover

    We scope a handover into every project: runbooks, a written description of what each AI part may do, and hands-on training for the people who run it, so your team does not depend on us.

  • Yours to keep

    By default, code, prompts, test sets and configuration live in your repositories, documented so another team could take over, with ownership set out in the contract. Support after launch is optional and agreed in writing.

  • Success in numbers

    Before we build, we agree what success means, such as time saved, error rate or cost per case, and measure today’s baseline. The same numbers decide go-live.

These are design practices, not certifications. We do not claim compliance with a standard on your behalf; we document what we built so your auditors and IT team can check it.

Questions about delivery

Will it work with our single sign-on and roles?

We design for it. AI parts get their own identity with only the access they need, and people see only what their existing permissions allow.

What happens when the AI is unsure or wrong?

Uncertain cases stop for a person, under rules agreed with you. Every action is logged, test sets catch regressions before a release, and the previous version stays ready to switch back to.

Can our own team take it over?

Yes. Code, prompts, test sets and configuration are in your repositories with runbooks, and handover includes training. Ongoing support is optional.

How do we know it worked?

We agree the numbers before the build, measure today’s baseline, and report against the same numbers after go-live.

Not sure which one fits?

The Build Blueprint is the first step when the problem is clear and the answer is not. It maps how the work runs today and ends in a build plan or a clear no.

See the Build Blueprint
  • A map of how the work runs today
  • Opportunities, ranked
  • The architecture we would use
  • Constraints: data, security, budget
  • Scope and a cost range
  • A build or no-build recommendation

Bring us the problem.

We start with the problem, then tell you whether it is worth building.

Bring us a bottleneck