AI Workflow Build · Fixed scope · Production-grade

Agentic AI workflows,
built and handed off.

Fixed-scope engagements to scope, build and deploy AI systems that take operational work off your team’s plate. Not consulting. Not slides. Production workflows running inside your business, documented, with your team trained to own them.

Engagement4–12 weeks
ScopeFixed at kickoff
ModelFixed fee
HandoffDocumented · team trained
Why this exists

Most teams know AI could help. Few have time to actually build it.

There’s a gap between “we should use AI for that” and a running system that actually does. Bridging it usually means a six-month consulting project, an in-house engineer you can’t hire, or an off-the-shelf tool that doesn’t quite fit. AI Workflow Build is the third option: a senior operator who designs, ships, and hands back an AI workflow tailored to your business, in weeks not quarters.

Common builds

Four patterns I most often ship.

Each pattern flexes to your business. The architecture stays consistent; the data sources, business logic and tone are yours.

Autonomous intelligence agents

Daily or weekly agents that scan multiple sources, reason against your business criteria, and surface a curated shortlist with explanations. Investor tracking, competitor monitoring, market signal detection, talent scouting.

  • Multi-source data ingestion
  • Structured suitability scoring
  • Reasoning explanations, not just lists
  • Delivered via email or Slack on schedule

End-to-end content pipelines

Listening → generation → publishing → reporting. The agent ingests signals, drafts post copy and titles, generates on-brand imagery, schedules and publishes, then returns a weekly performance report.

  • Social listening + signal extraction
  • LLM-driven copy and title generation
  • AI image generation aligned to brand
  • Multi-platform publishing + reporting

Operational automation

The repetitive work your team shouldn’t still be doing manually. Inbound triage and routing, content categorisation and tagging, document extraction, lead enrichment, supplier-data cleanup.

  • Classification and routing
  • Structured data extraction
  • Connects to your existing tooling
  • Human-in-the-loop where it matters

Automated reporting and insight

Replaces the Sunday-night report. Pulls from your systems, generates plain-language analysis, surfaces what changed and why it matters, and lands in your inbox before Monday standup.

  • Pulls from your existing data sources
  • Plain-language synthesis, not just charts
  • Highlights anomalies and trends
  • Delivered on the cadence you want
Builds I’ve shipped

Two production systems running today.

Both are deployed inside Le Collectif — my own venture in the hospitality space. The same architecture is what I build for clients.

Autonomous intelligence agent

Hospitality investor-tracking agent for Le Collectif.

The problem

Tracking active investors in a specific sector is a manual research task — scanning news, deal databases, funding announcements, fund websites and LinkedIn for signals. Done well, it takes hours per week. Done poorly, you miss live opportunities. Done not at all, you’re reactive.

The outcome

Every morning the agent surfaces three active investors, each with a reasoned explanation of why they fit the venture’s thesis. Not a database export — a reasoned shortlist that compounds pipeline visibility over time and never has an off day.

Approach

Agentic LLM architecture combined with proprietary scoring tuned to the venture’s thesis. Specifics of the data sources, scoring model and prompt design stay inside the engagement.

The shape of the build
  • Agentic LLM workflow with multi-step reasoning
  • Proprietary scoring tuned to the venture’s thesis
  • Scheduled daily execution and delivery
  • Built and deployed in weeks, not quarters

Full architecture, stack and methodology shared in scoping under NDA.

End-to-end content pipeline

Social listening, generation and publishing pipeline for Le Collectif.

The problem

Maintaining a meaningful social presence used to require a full-time social media manager or a paid agency. Both are expensive, and neither stays close enough to the business to spot the moments worth posting about.

The outcome

An always-on social rhythm that replaces a significant chunk of what a full-time social media manager does — without losing strategic intent. Listens, drafts, generates visuals, publishes, reports. The human stays in the loop for high-stakes posts and brand-defining moments; the agent runs the always-on cadence where they don’t need to be.

Approach

End-to-end pipeline from social signal to published content to weekly performance report. Built so the team retains creative control where it matters and the agent handles the rhythm where it doesn’t. Specifics of the architecture, tooling and prompt design stay inside the engagement.

The shape of the build
  • Listening and signal extraction
  • Generative content and visuals, brand-aligned
  • Scheduled publishing with human approval gate
  • Closed-loop weekly performance reporting

Full architecture, stack and prompt design shared in scoping under NDA.

How a build runs

From scope to handoff in four phases.

Every build runs the same arc, scaled to scope. The goal is a system your team owns — not a system that depends on me.

01 — Week 1

Scope

Define the workflow. Map the data sources, the decision logic, the output. Agree on what counts as “done.”

02 — Week 2

Architecture

Choose the stack. Design the agent’s reasoning steps and tool calls. Wire integrations. Confirm the build plan with you.

03 — Weeks 3–10

Build

Iterate. Build the agent, refine its prompts and tools, test against real data, tune until output quality is reliable.

04 — Final week

Hand off

Deploy. Document. Train your team to maintain and extend. Leave behind a system that runs without me.

Common questions

Before you ask.

What stack do you build on?
Production-grade LLM APIs (Claude, GPT, Gemini), workflow orchestration via Python or low-code platforms depending on what your team can maintain, vector databases where retrieval is needed, and standard cloud infrastructure (AWS, GCP, Cloudflare). Stack chosen to fit your team’s ability to own it after handoff, not to optimise for technical novelty.
Will my team actually be able to maintain it?
Yes — that’s the engagement’s explicit goal. Everything is documented. The final week is dedicated to training. The stack is chosen with your team’s skill ceiling in mind, not mine. If your team is non-technical, I’ll build on a stack they can use; if they have engineers, I’ll go deeper.
Who pays for the ongoing API and infrastructure costs?
You do, directly to the providers. I’ll estimate ongoing run cost during scoping (typically $30–$300/month for the workflows I build, depending on volume) and set up the accounts in your name so you never pay through me. No mark-ups, no surprises.
What if requirements change mid-build?
Minor adjustments are absorbed into the fixed fee. Material scope changes get a written change order with revised scope, timeline and fee — agreed before any work continues. No silent scope creep, no end-of-project surprises.
Can this combine with a Fractional COO engagement?
Often the strongest pairing. A Fractional COO engagement identifies the operational drag worth automating; an AI Workflow Build delivers the automation. Combined engagements are scoped together with a single timeline.
How do I know if my business is ready for this?
If you can name a specific operational workflow that’s costing you time or being done badly, you’re ready. If your answer is “I just feel like we should be using AI somewhere,” you’re not — and I’ll tell you so on the call. Genuine readiness is having a defined problem worth solving.
Start a conversation

Have a workflow in mind?
Let’s scope it.

30-minute exploratory call. Tell me the operational workflow you’re thinking about. I’ll tell you honestly whether it’s worth building, what it would take, and whether I’m the right person to build it.

Start a conversation Email me