AI Development Services · Done-For-You & Done-With-You
Ship AI-Native Software.
Not AI-Generated Debt.
Everyone can generate code now. Almost no one can keep it from rotting into a hallucination-prone mess by month three. I architect and ship AI-native systems — or embed with your team and install the workflow — using the functional patterns that make a codebase something the models actually reason about.
Two ways in: Done-For-You, where I build it, and Done-With-You, where your team keeps the capability.
Your AI Context Window Is Bleeding
The demo shipped in a weekend. Then reality hit. 400 lines in, the AI starts "forgetting." It hallucinates variables. It refactors working code into broken spaghetti. It fixes one bug and quietly creates two more — and now that mess is in production and your team owns it.
Why? Because the codebase is imperative junk. AI models are statistical engines, not compilers — every mutation and hidden side effect forces the model to simulate your program's memory in its head. That's what burns your tokens, stalls your roadmap, and turns "we ship with AI" into a maintenance liability.
// The Method
Functional Programming Is the Native Language of AI
This isn't Haskell or academic theory. It's the reason my builds don't rot — the three properties that turn a codebase into something an AI can read, refactor, and extend without hallucinating.
Functional Core, Imperative Shell — the only architecture that scales with AI agents instead of collapsing under them.
Context-Independent
Pure functions are atoms of logic an AI can read, understand, and refactor in isolation — without holding the state of the entire universe in its head. Same input, same output, zero surprises.
Hallucination-Proof
Discriminated unions and strict types constrain the model to valid code paths. If it compiles, it works. The AI literally cannot write an invalid state — type-driven development becomes your prompt engineering.
Token-Efficient
Declarative implementations compress complex logic into dense, high-meaning lines — effectively "zipping" your codebase for the context window. More capability per token, longer flow-state sessions, lower model spend.
The Same Feature, Two Ways the AI Sees It
This is the difference between a codebase your AI accelerates and one it slowly destroys. It's what I build for you, and what I train your team to write by default.
Imperative — the AI gets confused
let total = 0;
let items = getItems();
for (let i = 0; i < items.length; i++) {
if (items[i].active) {
total += items[i].price;
items[i].counted = true; // mutation
}
}Hidden state, mutation, and implied ordering. The AI must track every variable across the whole function to touch a single line safely.
Functional — the AI optimizes instantly
const total = (items) =>
items
.filter(item => item.active)
.reduce((sum, item) => sum + item.price, 0);
const grandTotal = total(getItems());Pure, immutable, composable. Each function reads in isolation — the AI refactors it aggressively and safely, because there's nothing hidden to break.
// Two Ways to Work Together
Done-For-You or Done-With-You
Same method, two delivery models. Hand it off and I ship it — or embed me with your team and keep the capability in-house forever.
I Build It & Ship It
You have a product to ship, an MVP to prove out, or a legacy system quietly hemorrhaging money. I architect it, build it AI-native from the first commit, and hand you production software that stays maintainable long after launch.
- New product & MVP builds — React, Redux Toolkit, TypeScript, Rust
- Legacy rescue & modernization via the strangler pattern — no big-bang rewrite
- AI features done right — agents, RAG, and inference on a functional core
- Functional Core / Imperative Shell architecture, typed against invalid states
- Property tests and CI that keep future AI edits honest
- Delivered as clean, documented, AI-readable code your team inherits
I Embed & Level Up Your Team
Your developers already ship with Cursor, Windsurf, and Claude — but the output is inconsistent and the codebase is drifting. I embed alongside them, install the AI-native workflow, and pair until writing hallucination-proof code is your team's default, not my party trick.
- A production
.cursorrulesandCLAUDE.mdtuned to your stack - Live pairing on your real repo — not slideware
- Team workshops on the functional patterns AI actually rewards
- Architecture guardrails and PR standards that survive after I leave
- A reusable prompt library for refactoring legacy spaghetti into clean pipes
- Your team keeps the capability — the whole point is to make me optional
Not sure which fits? Most engagements start Done-With-You and graduate to Done-For-You once the standard is set — or the reverse. We'll figure it out on the call.
How an Engagement Runs
Same four beats whether I'm building for you or with you. No twelve-week discovery phase — we're touching real code in week one.
Audit
A free architecture call, then a hard look at your codebase and AI workflow. I map where the model is bleeding tokens and where the debt is compounding.
Architect
A functional core, a typed domain that can't represent invalid states, and the rules files that make every future AI edit safe. A plan sized to your goals, not a document that collects dust.
Build / Embed
DFY: I ship features in tight, reviewable increments. DWY: I pair with your team on the same increments so the patterns stick. Either way, working software every week.
Hand Off
Documented, tested, AI-readable code plus the workflow that keeps it that way. You leave with a system that stays maintainable whether or not I'm still in the room.
The Playbook Behind the Work
Every engagement draws on the same pattern library — a pattern-first path from your simplest pure function to event-sourced architecture, the same functional patterns running in production in the ForbocAI neuro-symbolic AI SDK. In DWY, this is what I teach your team.
- Pure functions as the atom of AI-readable logic
- ES6+ features for functional programming
- TypeScript & the type system as a guardrail
- Redux Toolkit the functional way
- Functional composition & point-free pipes
- Thin components, fat reducers
- Monads & safe error handling (Maybe/Either)
- Discriminated unions that block invalid states
- Category theory, just enough to be dangerous
- Memoization & virtualization
- RTK Query, caching & optimistic updates
- The same patterns across Rust, Python & beyond
- Functional Core, Imperative Shell
- CQRS & event sourcing
- Strangler pattern for legacy rescue
- The rules file & automated architecture checking
- Refactoring prompts & reducer generation
- Property testing & zero-mocking TDD
What Ships With Every Engagement
You don't just get code or a workshop — you get the whole system that keeps a codebase AI-native after I'm gone.
Tuned Rules Files
A production .cursorrules and CLAUDE.md that force pure, immutable, typed code by default — calibrated to your exact stack.
A Functional Core
A typed domain and pure-function core the AI can extend safely for years — the load-bearing architecture, not just the demo.
Tests & CI Guardrails
Property tests and pipeline checks that catch the next hallucinated regression before it merges — zero-mocking TDD baked in.
A Prompt Library
Copy-paste reasoning prompts for refactoring legacy spaghetti into clean pipes and generating reducers your architecture accepts.
The Team Playbook
The pattern-first curriculum your developers keep — from the simplest pure function to event sourcing, with impure-vs-pure comparisons throughout.
PR & Architecture Standards
Review checklists and architecture rules that keep the whole team shipping to the same bar long after the engagement ends.
Who I Work With
The Founder
You have a product to ship and no eng team yet. You want it built right the first time — DFY, so you launch on architecture that scales instead of a prototype that has to be thrown away.
The Engineering Team
Your devs ship with AI but the output is inconsistent and the debt is piling up. DWY installs one standard everyone writes to — and levels up the whole team while doing it.
The CTO / Eng Lead
You need a defensible standard before "we ship with AI" becomes an unmaintainable liability. I set the architecture, the guardrails, and the workflow — and rescue what's already drifting.
Built From Production, Not Theory
Two decades of shipping, and every pattern I install runs in production — including the ForbocAI neuro-symbolic SDK, where Redux-style state, pure functional pipelines, and monadic inference drive autonomous game NPCs.
Refactored a legacy storefront to modern standards — 70% faster, 28% higher conversion, roughly $73,500 in additional monthly revenue.
Modernized a core platform into a maintainable codebase — $27,300/mo in saved maintenance plus $35,000/mo in operational efficiency.
Custom architecture cut 30+ hours of weekly manual work and eliminated $31,200/mo in compliance penalties and rework.
Engagement Options
Three ways to start. Every one opens with a free architecture call — if I can't spot AI-native wins worth far more than the fee, you'll know before you spend a dollar.
AI Advisor
- Monthly architecture & roadmap sessions
- Rules-file governance & code reviews
- On-call guidance when the AI goes sideways
- Best for teams that need a standard-bearer, not a builder
Embedded AI Architect
- Weekly pairing & implementation alongside your team
- The full AI-native workflow installed and enforced
- Architecture reviews & legacy debt resolution
- I ship features and level up your devs in the same sprint
AI Build & Rescue
- End-to-end product & MVP builds
- Legacy modernization & strangler-pattern rescue
- AI features — agents, RAG, inference — on a functional core
- Priced to the outcome, not the hour
Prefer a fixed-fee two-week enablement pilot to prove the ROI before you commit? Ask about it on the call.
Frequently Asked Questions
What’s the difference between Done-For-You and Done-With-You?
Done-For-You means I architect, build, and ship the software myself and hand you production-ready code. Done-With-You means I embed alongside your team, install the AI-native workflow, and pair until your developers own the capability — the goal is to make me optional.
How fast can you start?
Every engagement opens with a free architecture call. From there, most builds and embeds begin within a week or two once scope is agreed.
What stacks do you work in?
TypeScript, React, Redux Toolkit, Node, and Rust — always on a functional core with an imperative shell, typed against invalid states so AI edits stay safe.
Do you work with existing or legacy codebases?
Yes. Legacy rescue and modernization run through the strangler pattern — incremental, reviewable increments — so there’s no risky big-bang rewrite.
What does it cost?
The AI Advisor engagement starts at $5,500/mo and the Embedded AI Architect at $9,500/mo; Done-For-You builds are fixed-scope and quoted on the call. If the architecture call doesn’t surface wins worth far more than the fee, we don’t move forward.
The AI revolution isn't about typing faster. It's about shipping systems that don't rot.
Whether I build it for you or install the standard with your team, you walk away with software the AI keeps making better — not worse.
Start with a free architecture call. Bring your messiest repo.
No Excuses. Just Systems That Ship.
If the architecture call doesn't surface AI-native wins worth far more than any engagement fee — faster shipping, lower model spend, or debt you can finally stop paying interest on — we don't move forward. No strings attached.