Multi-agent systems, LLM integration, and internal tooling for companies that already know what they need and cannot hire anyone who can ship it.
One engineer, no account layer. £500-750/day, €10-25k implementations, €2,500-5,000/mo to run them. Remote across the UK, US and Europe.
The strategy decks are done. The vendor demos were impressive. Nothing is running. This is the gap between an AI proof of concept and a system your operations team depends on.
The pilot never survived contact with auth, rate limits, error handling, and real data volume.
Engineers who have actually shipped agent systems are rare, expensive, and not on the market long.
An LLM with no access to your ERP, CRM, or document store answers questions. It does not do work.
Model behaviour drifts, costs creep, edge cases surface. Without evaluation and monitoring, quality degrades quietly.
The work is the unglamorous part: integration surfaces, evaluation, cost control, and failure modes. That is what gets a system live and keeps it there.
Engineering work, scoped and shipped. Not workshops, not strategy decks.
When one model call isn't enough and a chain of them isn't reliable.
Specialised agents with defined responsibilities, shared state, and a control layer that decides what runs, in what order, and what happens when a step fails.
When the model has to work inside software you already run.
Connecting language models to your ERP, CRM, ticketing, and internal APIs, including the auth, rate limiting, and schema mapping that pilots skip.
For the team doing the same judgement call two hundred times a week.
Purpose-built interfaces where a model does the first pass and a person approves, corrects, or overrides. Built for the specific workflow, not a general chat box.
When the answer exists somewhere in twenty years of files.
Ingestion, parsing, chunking, embedding, and retrieval built around your document types and access rules, with measurable retrieval quality rather than assumed quality.
For systems already live that nobody internally is watching.
Build it and run it. Monitoring, evaluation against real traffic, model and cost tuning, and iteration as your data and the underlying models change.
A multi-agent system I built and run. It operates a real business every day. Everything described in the services above exists here first.
Each agent owns a domain and has its own tools and instructions. All five read and write the same persistent store, so context carries between agents and across sessions instead of resetting every conversation.
Capabilities reach the agents as schema-typed tools served by MCP servers, not as hard-coded prompt text. The tool surface changes without rewriting agent logic, and every call is logged.
Vector similarity, keyword search, recent high-importance facts, the cross-agent activity log, and the current thread — merged into one context block per prompt. It also flags contradictions instead of silently picking a side.
Gmail, Google Calendar, Stripe, GoHighLevel and Telegram. Real inbox, real calendar, real payments. There is no staging environment where a failure goes unnoticed.
A Next.js dashboard on Supabase, a Node service layer holding the agent runtime and integrations, and a Python bridge for voice. The runtime spawns the Claude CLI as a subprocess through the Agent SDK — it is not a chat wrapper over an API call.
In continuous operation for months against live accounts, not a demo instance. It has already survived model version changes, API deprecations, and the edge cases nobody scopes at the start.
export function cosineSimilarity(a: Float32Array, b: Float32Array): number {
if (a.length !== b.length) return 0
let dot = 0
let na = 0
let nb = 0
for (let i = 0; i < a.length; i++) {
dot += a[i] * b[i]
na += a[i] * a[i]
nb += b[i] * b[i]
}
const denom = Math.sqrt(na) * Math.sqrt(nb)
return denom === 0 ? 0 : dot / denom
}
AIOS is not a product and it is not for sale. It is the reference implementation: evidence that this architecture has been built end to end by one person and kept running under real load. Systems like it get built for clients as bespoke internal tooling.
One fixed-price entry point. Everything after it is scoped from what the audit finds, not from a guess.
Fixed price, fixed scope, fixed timeline. You keep the output either way.
Maps your processes, identifies what is actually automatable with current models, and returns a costed build plan. You own the plan whether or not you continue.
For teams that need embedded engineering or technical direction rather than a fixed deliverable. Booked in blocks, minimum three days.
Scoped from the audit. Ranges reflect integration count and data complexity, not headcount.
Building and shipping the system the audit specified. Typically four to ten weeks, delivered in reviewable increments rather than one handover at the end.
Agent systems drift as models and your data change. These are for teams that would rather not staff for that.
Monitoring, evaluation, and iteration on a system already in production.
Reserved capacity for companies running several systems or expanding into new functions.
The audit is the only fixed price on this page, and it is deliberately the only one. Implementation and retainer figures are ranges because scope depends on how many systems have to be touched and how clean the data is — both of which the audit establishes. Day-rate work is available without an audit where you already know what needs building.
Three structural differences, not values statements.
You brief the engineer and the engineer writes the code. Nothing is relayed through an account manager or passed to a subcontracted team you never meet.
A demo is not a milestone. Engagements are measured by what is running against real traffic with monitoring attached, not by what was shown on a call.
Code and infrastructure live in your accounts. The audit plan is yours whether you continue or take it to someone else. No hosted black box you cannot leave.
Three stages. The first one is priced and the second one is scoped from it.
Two weeks mapping processes and integration surfaces. Output is a costed build plan naming what is automatable, what is not, and in what order.
Implementation in reviewable increments. Evaluation and monitoring ship with the system rather than being added after something breaks.
Handover to your team, or managed ops if you would rather not staff for model drift, cost creep, and new edge cases.
Audit: two weeks. Implementation: four to ten weeks depending on integration count. Remote across UK, US and European time zones, with overlap hours agreed before work starts.
Two weeks, fixed scope, €3,500. It maps your current processes, identifies what is actually automatable with current models, and returns a costed build plan with sequencing and risk. You own the plan whether or not you continue with the build.
One engineer, directly. There is no account manager layer and no offshore delivery team. You talk to the person writing the code. That is a constraint as well as a feature: capacity is limited and scheduling reflects it.
Yes. Engagements run remotely across UK, US and European time zones, with overlap hours agreed at the start. Most implementation work is asynchronous with scheduled review calls.
Demos fail on the parts nobody scopes: auth, rate limits, error handling, evaluation, cost control, and the integration surface of systems that were never designed for agents. The audit prices those in before anyone writes code, and the build treats them as the work rather than as edge cases.
Implementations run €10,000 to €25,000 depending on scope, typically four to ten weeks. Day-rate engagements are £500 to £750 per day where you need embedded engineering rather than a fixed deliverable.
Managed automation ops runs €2,500 to €5,000 per month: monitoring, evaluation against real traffic, model and cost tuning, and iteration. Agent systems drift as models and your data change. Handing over a repository and walking away is how they degrade.
Every build is a separate deployment with its own database. There is no shared tenancy and no pooled data — the system runs in your cloud account, or one provisioned for you and handed over. Documents and internal data are never used to train external models, and data boundaries are agreed in writing before any build starts.
€3,500, two weeks, fixed scope. A costed build plan you own regardless of what you do next.
Start the AuditThirty minutes on what you have tried, what stalled, and whether this is worth either of our time.
Book a Technical CallReplies within one business day.