Build an AI system
Assessment, then a scoped build: one workflow taken from manual to autonomous, in production, with the dashboard and the runbook.
- Assessment
- 1–2 weeks
- First release
- 4–8 weeks
- Price
- Fixed, itemised
Line 02
AI systems that do the work.
Agents and models deployed inside your own systems, acting on your own data, with every action logged and reversible. Built for operations where a wrong answer has a cost.
- 15+ systems in production
- Every action audited
- You own the code
Product tour
Watch one run, end to end.
Two real shapes of work, replayed step by step: an agent clearing a finance exception, and MASH Creative producing a campaign. Nothing here is a screenshot — it is the sequence a live system actually follows.
SequenceAgent run
Step 01 · Intake
A job arrives on its own.
The agent subscribes to the exception queue. Nobody forwarded this, nobody opened a ticket.
- Exception raised: PO-4471 invoice total mismatcherp.exceptions · queue ap-01 · 09:04:12Event
- Queue wait
- 1.2s
- Human time
- 0 min
- Cost so far
- $0.004
Bring your own workflow and we will walk the same five steps against it — on your data, in a working session.
Book a walkthroughScope
Three things we build.
Most engagements start with one and grow into the others. All three are custom — there is no MASH platform you have to adopt. Pick one to see what a first build looks like.
Selected
Autonomous agents
Software that takes a task end to end inside your stack, then hands back a record of what it did — with a human gate wherever the cost of being wrong is high.
Typical first buildInbox and exception triage for one team, live in production.
What it includes
- Inbox, ticket and order triage
- Quote, report and document drafting
- Reconciliation and exception chasing
- Scheduled follow-up across systems
- Live in
- 4–8 weeks
- Human gate
- Always
- Rollback
- One click
Method
Grounded, constrained, observed.
A demo is easy. A system that runs unattended on Monday morning is the job. Four rules make the difference.
- Step 01
Ground it in your data
Retrieval over your records, not the open web. Answers cite the source row or page.
- Step 02
Constrain the actions
An agent gets a fixed set of permitted operations, limits, and a human gate on the rest.
- Step 03
Deploy in your estate
Your cloud, your tenancy, your region — or ours if you would rather not run it.
- Step 04
Observe everything
Cost, latency, accuracy and every action on a dashboard — with alarms when they drift.
Guardrail sandbox
Ask the agent to do something.
Policy is written before the first line of code. Pick an action and see what the system would do with it.
Allowed
It sends, from the shared mailbox.
Templated customer comms sit inside the permitted set. The draft follows an approved template and the thread stays in your mailbox.
- Signs off
- Nobody — pre-approved
- Logged as
- comms.customer.sent
Non-negotiable
- Your data stays yours
- Every action audited
- Human override always
- No lock-in, you own the code
Add agents to a live system
You already have the platform — ours or someone else's. We add the model layer, the guardrails and the observability without a rewrite.
- Review
- 1 week
- First agent live
- 3–5 weeks
- Operating
- Monthly, cancellable
MASH Creative
Our own AI system for marketing operations — the method above, running in production. Built for one team, now a product.
Next step
Bring one workflow. We will tell you if it should be an agent.
Half the time the honest answer is a script, an integration or a fixed report. You get that answer either way.
