AI systems for real operations.

For small operators who need useful systems, not demos. From simple automations to more structured AI systems.

Pierre Builds founder portrait

What I build

Systems that actually take work off your plate.

Internal operations

Research, reporting, monitoring, follow-up, and repetitive coordination work turned into reliable workflows.

Tool-connected automation

Systems wired into the tools you already use: APIs, docs, dashboards, spreadsheets, CRMs, and messaging surfaces.

Validation and continuity

Memory, handoffs, audit trails, and truth checks so the system stays usable over time instead of drifting into guesswork.

Proof

What I built for myself

Over 6 months, I built an autonomous multi-agent trading research system for Polymarket.

The system used a researcher + engineer architecture, file-based memory, structured handoffs, Chainlink integration, and validation / reconciliation layers to test whether the strategy was real or just badly measured.

The conclusion was honest: the bot did not find durable retail edge in 5-minute crypto markets. The valuable output was the architecture and operating discipline that made that conclusion trustworthy.

Two-agent architecture diagram

One example of the work: the system initially reported 90% accuracy. After rebuilding the measurement layer, the real figure was closer to 60%.

Why the bot was lying to me comparison diagram

How I work

Simple process, serious implementation.

01

Map the workflow

We identify where time, context, or decision quality is currently leaking so the project solves a real problem.

02

Scope the right level of automation

Sometimes that means a simple script. Sometimes it means a more structured AI system. The scope should match the job.

03

Build and integrate

I connect the system to the real tools, data, and surfaces the work actually depends on, not a demo environment.

04

Validate and hand over

The output has to be trustworthy, understandable, and usable by you after delivery. Includes 30 days of post-launch support.

Pricing

Typical engagement range

Enough transparency to qualify the conversation, without pretending every project fits one fixed box.

Audit / scoping

From £750

Discovery, workflow diagnosis, and a written implementation roadmap for the highest-value next step.

Implementation

Typically £1.5k–£5k

Build, integration, testing, handover, and support sized to the complexity of the system.

Typical ongoing costs are low and paid directly by the client: hosting is usually modest, and API spend depends on usage.

Contact

Building something similar for your business?

I work with small operators who need serious systems, not demos.