AI automation · integration · coaching
AI that does the work, not just the demo.
Fundly AI Consulting turns AI from a browser tab into infrastructure — codified SKILL.md automation, editor and platform integrations, production APIs and backends — then teaches your team to actually use it. Technical depth for engineering. Plain English for everyone else.
No obligation. You'll leave the call with a written recommendation whether or not you hire us.
The gap
Most organisations have AI access. Very few have AI leverage.
Licences got bought. A few people got excited. And then it stalled — because the hard part was never the model.
The same prompt, retyped forever
Your best people have a 600-word prompt in a notes app that they paste in every Monday. It works — for them, on their machine, until they leave. Nothing about it is repeatable, reviewable, or shared.
AI that can't reach your systems
The model is smart but blind. It can't see your CRM, your ticketing system, your database, or the folder where the actual work lives — so every task starts with a human copying context in and copying results out.
A pilot that never became a product
Someone built a compelling notebook demo. Then came auth, rate limits, cost controls, evaluation, logging, and error handling — and it quietly stopped being anyone's job.
The pattern we fix: AI knowledge trapped in individuals instead of encoded in systems. Our whole practice is moving that knowledge out of heads and browser tabs into version-controlled, testable, shareable assets — and then making sure people know how to use them.
What we do
Four practices. One throughline.
Each stands alone. Together they take an organisation from "we have ChatGPT licences" to "AI is part of how we operate."
Agent Skills & SKILL.md automation
We take the workflows your experts run from memory and encode them as Agent Skills — versioned SKILL.md folders with instructions, reference material, and executable scripts that load only when relevant. Authoring, progressive disclosure design, testing, org-wide libraries, governance, and rollout.
Extension & integration support
Getting AI where the work already happens: VS Code and JetBrains, Chrome extensions, Slack and Teams, MCP servers that expose your internal systems as tools, plus Jira, Salesforce, HubSpot, Notion, GitHub, Google Workspace. Auth, permissions, and rate limits included — not deferred.
Integrations, in depth →API & backend development
The unglamorous layer that makes AI dependable: streaming endpoints, tool-use loops, retrieval, prompt caching, cost and rate-limit controls, evals, observability, and CI. Python, TypeScript, Go. Deployed to Cloudflare Workers, AWS, GCP, Azure, or your own metal.
Backend work, in depth →AI coaching for non-technical teams
No jargon, no code, no homework. Live sessions built around your actual job — inbox, documents, spreadsheets, meetings, planning — plus what AI gets wrong, what never to paste into it, and how to check its work. For ops, finance, HR, marketing, legal, sales, and leadership.
Coaching programmes →How engagements run
Small, fast, and yours to keep.
No six-month discovery. We start with the narrowest thing that would obviously be valuable, ship it, and expand from there.
Scope (free, 30 minutes)
We map where the time actually goes and pick one workflow worth automating. You get a written recommendation — including "don't build this" if that's the honest answer.
Pilot (1–3 weeks)
One skill, one integration, or one endpoint — built properly, not as a throwaway. Fixed price, fixed scope, working software at the end.
Expand
Second and third workflows go faster because the patterns, evals, and infrastructure already exist. This is where the compounding shows up.
Hand over
Source code, documentation, runbooks, and a working session with your team. You can maintain it without us. Most clients keep a retainer anyway — but by choice, not by lock-in.
For everyone who isn't an engineer
AI coaching in plain English.
If "prompt engineering", "context window", and "RAG" make your eyes glaze over — good. You don't need any of that. You need to know which parts of your job AI is genuinely good at, how to ask for what you want, and how to spot when it's confidently wrong.
Sessions are hands-on and use your own work: the reports you write, the emails you dread, the spreadsheets nobody wants to reconcile, the meetings you have to summarise. Everyone leaves with three habits they'll use on Monday.
What people actually leave with
- A personal list of the 5 tasks in your role AI handles well
- A repeatable way to ask that gets a usable answer first time
- A clear, memorable rule for what never gets pasted into an AI tool
- A 60-second method for checking whether an answer is trustworthy
- Your own saved prompts and templates — set up during the session
- Honest limits: the things AI is bad at, so you stop wasting time on them
Also in demand
The work clients keep asking for next.
These usually start as a follow-on to a first project, and just as often become the main engagement.
AI readiness & policy
A written, human-readable AI use policy, a data-handling rulebook, an approved-tools list, and a risk register. What most compliance, legal, and insurance reviews now ask for — and what most teams don't have.
Document & data pipelines
Contracts, invoices, PDFs, forms, and email attachments turned into structured, validated data with a human review step where it matters. Usually the fastest measurable ROI in the building.
Internal chat over your own knowledge
Retrieval that cites its sources and says "I don't know" — over your handbook, wiki, policies, tickets, and past projects. Built with permissions respected, not bolted on afterwards.
Cost & usage control
Bill gone sideways? Caching strategy, model routing, batch processing, effort tuning, and per-team budgets and dashboards. Frequently pays for the engagement outright.
Evaluation & quality harnesses
A test suite for non-deterministic systems: golden datasets, LLM-as-judge scoring, regression gates in CI. This is what lets you change a prompt or a model without holding your breath.
Fractional AI lead
A few days a month of senior capacity: reviewing vendors, unblocking engineers, setting standards, and telling leadership the truth about what's feasible and what isn't.
Straight answers
Questions we get first
We're not a tech company. Is this for us?
Especially for you. The highest-return AI work is rarely in engineering — it's in operations, finance, admin, HR, and customer service, where the same document-shaped task repeats hundreds of times a month. The coaching practice exists precisely because the people who benefit most are usually the least served by AI vendors.
We already pay for AI tools. Why hire a consultant?
A licence gives you access; it doesn't give you leverage. Most teams use maybe 10% of what they're paying for, because nobody's job is to encode the workflows, connect the systems, or teach the habits. That's the whole gap we close — and if a tool you already own solves the problem, we'll tell you and stop there.
How do you handle our data and security?
Default to the least data that does the job. We work in your environment where possible, never move production data without written approval, use environment-scoped secrets that never touch client code, and document exactly what is sent where. If you have a DPA, security questionnaire, or zero-retention requirement, bring it to the scoping call — it changes the architecture, so it should be decided early rather than retrofitted.
Which AI models and providers do you use?
Whatever fits the job and your constraints. Anthropic's Claude, OpenAI, Google, or open models you host yourself — we're not tied to a vendor and take no referral fees. Just as often the right answer is a cheaper, faster model with better engineering around it than a frontier model with none.
What if AI turns out to be the wrong tool?
Then we say so. A surprising share of "AI problems" are really a missing report, an unindexed database, or a broken form. Recommending a two-hour fix over a two-month project costs us revenue and buys us a client who trusts us — that trade has never once looked like a bad deal.
Let's find your first win.
Thirty minutes, no pitch deck, no obligation. Bring one workflow that eats more time than it should and we'll tell you honestly whether AI can fix it — and roughly what that would take.