AI that answers every lead and plugs the leaks. Fixed price. You pay after the audit.
I build the AI and automation that catches the enquiry your front desk missed, chases the quote that went cold, and does the manual work that quietly caps what you earn. Fixed scope, fixed price, from $1,500 CAD - and you see a written audit before any money changes hands. Running in under a week, async only, no calls.
Get a free teardown of your bottleneck → See it first: what this looks like in a business like yours →Free written teardown of your one worst bottleneck - what I would automate, what it would take, and a fixed price. No call, no obligation. You only pay if you decide to do the work.
The businesses where AI pays for itself in one month.
If you sell something high-value, a missed lead or a slow follow-up is not a small loss, it is a four-figure one. These are the rooms where a system that never drops a lead and never forgets a follow-up turns into real money fast. The picture is the work I build for each. The math underneath is your own numbers, not a promise.
Every enquiry booked, even the 2am one
High-ticket treatments, enquiries from six places, a front desk that closes at 5. The DM that comes in on Sunday night either gets answered in seconds by your system, or it becomes your competitor's patient. I build the layer that captures, replies, books, and reminds, so the lead you paid an ad to get never sits unanswered.
Change the price once. Trust all 40 stores got it.
The bulk update that fails halfway is the nightmare: store 14 keeps the old discount, store 22 loses the data, and you find out from an angry franchisee. I build the update layer so a change is all-or-nothing across every location, validated before it commits, with one batch ID that proves the whole set landed or rolled back clean. This is the exact pattern from my published case study.
The quote that chases itself until it closes
You send a five-figure quote and then you are busy doing the actual work, so the follow-up happens from memory, or not at all. Most quotes are not lost to a no, they are lost to silence. I build the sequence that nudges every open quote on its own schedule, in your voice, and stops the instant they reply or decline. You stay the expert. The chasing stops being your job.
Don't see your business? If you sell something high-value and lose any of it to manual work, the math works the same. Tell me what is leaking →
The job posting you keep writing. It is me.
Teams in 2026 are posting for one of these titles, then waiting months for a hire who can actually ship. I do every one of them, end to end, as a fixed-scope engagement - no headcount, no onboarding, running in a week.
Applied AI / AI Engineer
I build LLM features that survive production, not demos: RAG pipelines, retrieval, embeddings, prompt and context engineering, the whole path from data to a shipped endpoint.
What this looks like →
Agentic AI Engineer
I design multi-agent and tool-using systems - function calling, orchestration, guardrails - that take real actions safely instead of just answering. Apollo Finvest ran 27 tools in a dual-mode agent.
What this looks like →
AI Solutions / Enterprise AI Architect
I architect the whole system: vendor-agnostic model routing, data model, compliance, the build-vs-buy calls. I delivered a multi-vendor agent under RBI / DPDP rules in 3 days.
What this looks like →
Forward Deployed AI Engineer
I drop into your real environment and get the AI working against your data and your constraints - the customer-facing build that closes the gap between a prototype and something that holds up live.
What this looks like →
AI Evaluations & Reliability Engineer
I build the evals, guardrails and observability that tell you whether your agent is actually right - and I audit AI-generated systems before they break in front of users.
What this looks like →
AI Cost & Spend Engineer
I make agent spend predictable: estimate a run's cost before it executes and hard-stop it at a cap. I built Runcap to do exactly this - the skill most teams discover they need only after the bill arrives.
What this looks like →
AI Automation Engineer
I turn manual, multi-step workflows into automation that runs itself - n8n, custom orchestration, LLM-in-the-loop - on top of the tools you already use.
What this looks like →
AI Security & Hardening Engineer
I find where AI-coded systems leak: broken auth, exposed secrets, prompt-injection surface, missing permissions - and I close them with a written audit before you pay.
What this looks like →Same person, same fixed-scope terms across all of it. The proof below is real client work, not a list of skills - see the builds →
Two of these come up more than the rest. Pick the one that sounds like your week:
Each build leads with a method most teams serving small operators are not using yet.
Not buzzwords - specific, named techniques that became practical in the last year. This is the difference between an AI build that ages out in a quarter and one designed on where the field actually is now.
You pay only after the audit document is in your inbox.
Day 1 you email me. Day 2 you get a written audit document detailing every issue I found, with severity, root cause, and the exact fix. You read it. If you decide the fixes are not worth doing, you keep the audit and we are done. No half built work, no awkward refund conversations, no time wasted. The first money changes hands only after the audit lands.
Your AI-coded app shipped. Now it is breaking where you can't see it.
You built the MVP with Cursor, Claude, or Lovable and it worked in the demo. In production the auth leaks, the webhooks drop payments, and the first enterprise security review kills your biggest deal. I find the 5 things that break first and I fix them, with a written audit in your inbox before you pay anything.
Broken auth and missing row-level permissions, webhook handlers that silently 500, secrets in the client bundle, schema with no error handling, deploys that can't roll back.
45% of AI-generated code ships with security flaws (Veracode 2025). The bug you can't see is the one that loses revenue or fails the review that closes your deal.
The work is there. You are losing it to slow, manual follow-up.
HVAC, plumbing, roofing, renovation, property management. Quotes go cold because nobody chased them, leads fall through the cracks between text, email, and a spreadsheet, and the one person who holds it all in their head is the bottleneck. I build a follow-up and intake system that runs on its own, on top of the tools you already use. No new app to learn.
Automatic quote and invoice follow-up, missed-lead recovery, intake that routes and prioritizes itself, clean daily view of what is open and waiting. Built on your existing stack.
Every cold quote is revenue you already earned the right to win. The follow-up that worked at 10 jobs a month quietly breaks at 30, and you only notice in the numbers.
Three productized packages. One custom track.
Pick one, send me your stack and what is breaking, get a free written teardown and a fixed price within 24-48 hours.
Vibe-Code Audit + Stabilization
For solo SaaS founders post launch with shaky foundations. I find the 5 things that will break first (auth, schema, error handling, deploys, secrets) and I fix them. You get a written audit on day 2 before any further payment. Real fixes by day 7.
Reply Chaser Automation
For any business losing leads or revenue to slow follow up. Stripe webhook recovery, abandoned trial nudges, sales sequence automation, churn signal tracking. Built on n8n or your existing stack. Runbook included. You own the system end to end.
Multi-Location Config Governance
For businesses running pricing, promos, or settings across many stores, branches, or franchises. The risk is a one-click bulk update that fails halfway and silently corrupts half your locations. I design the update layer so multi-location changes are all-or-nothing: resolve the full change set, validate every value, commit them in a single transaction, and stamp one batch ID so you can prove the whole set committed or rolled back. Inheritance from a parent location to its children handled cleanly. Read the pattern →
Custom AI / n8n / Automation
Bigger or more specific builds. Scoped in writing first, fixed price after we agree. Recent shapes: 2-system n8n integration ~$3,000. Multi-agent reasoning bot with tool use ~$5,500. Full internal ops dashboard with AI summaries ~$7,500.
Four steps. No discovery calls. No surprises.
You email me
Stack, what is breaking, what you have already tried. Three things. No form to fill.
I respond in 24h
Fixed price for your case, or a referral elsewhere if you are not a fit. No calendar link.
Audit delivered
Written audit document in your inbox. You pay only after you read it.
Fixes shipped
Real changes in your codebase. Runbook so the same things do not break again.
Two builds I can actually show you.
I am early and I am not going to invent client logos. These are two real, recent engineering projects - one I can name, one I can't yet name. Both are systems built fast under real constraints, with the architecture and code to back them up.
Apollo Finvest: a compliant AI agent for an NBFC, in 3 days
A take-home build for an Indian non-bank lender (NBFC). In three days I shipped a working React Native + Expo app with a dual-mode AI agent (one mode for customers, one for the internal team) running 27 tools. It uses multi-vendor AI - Gemini 2.5 Flash as primary, Llama 3.1 on Groq as a one-env-var fallback - so a single provider outage can't take it down. Built to India's RBI digital-lending and DPDP Act privacy rules, with an anonymous mode and a hashed audit chain. Designed to run near $0.05 per customer per month at 100K daily users. The reviewer's response: "looks good", followed by GitHub collaborator invites.
Multi-location config governance for a retail chain
A design for a retail operator running 15 to 50 stores that needed to push pricing, promo, and tax changes across many locations at once - safely. The trap in the naive build: a one-click bulk update loops through stores, and when it fails halfway, half the locations get the change and half keep the old value, with nothing to say which is which. I designed the update layer to be all-or-nothing: resolve the full change set first (including stores that inherit settings from a parent location), validate every value, commit them in a single transaction, and stamp one batch ID so you can prove the whole set committed or rolled back. Built on Dataverse with a server-side transactional commit. Read the full pattern →
Client name on the second one is held back until a decision lands. Honesty is the point: I would rather show you two real builds than four anonymous stories you can't check.
Runcap: control AI coding spend. Require proof before merge.
Part of the same system. Runcap is a local-first control layer for AI coding agents. It can cap requests routed through its local gateway and require a GitHub Proof Gate to replay permitted AI-generated pull-request changes against base-commit policy and verification before merge eligibility.
Who I am NOT for.
Saying no early saves both of us a week. If any of these is you, hire someone else.
- Not for retainers. I do single fixed scope engagements. If you want monthly support, hire someone else.
- Not for free consultation calls. I do not have a calendar link. If you want to know if we are a fit, send me an email.
- Not for other consultants looking for subcontractors. I do my own work end to end.
- Not for someone who needs hand-holding through video calls. I am async only. We work over text and email.
- Not for SOC 2 or ISO compliance. I fix the foundation, I am not a security certification firm.
- Not for 3 month rewrites. If the answer is "rebuild from scratch", I will tell you that and refer you somewhere.
Tell me your one worst bottleneck. Get a written teardown in 24-48 hours.
No call. No calendar link. You describe the thing that keeps breaking or eating your time, and I send back a short written assessment: the likely bottleneck, where it is most likely to fail, the approach I would take, a rough scope, and a fixed project price - free, before any money changes hands. You only pay if you decide the fix is worth doing.
The free teardown is an assessment, not a full code audit, penetration test, compliance opinion, implementation, or production certification. If you need the deeper version - a paid technical audit with severity ratings, root cause, reproduction steps, risk prioritization, and a remediation plan - I deliver that in 72 hours as a paid diagnostic that can precede a stabilization engagement.
What is slipping through the cracks?
Or email directly: kirill@launchsoloai.com
One person. Built things under pressure before AI was a buzzword.
Kirill D.
Calgary based. I build and stabilize automation systems for solo founders and small teams. My focus is the boring expensive stuff: webhook reliability, auth that survives security reviews, multi-step AI reasoning flows that do not fall over in production.
Before this, I spent five years leading high-stakes public sector operations. Multi-disciplinary teams of ten plus people, multi-month campaigns, crisis communications, multi-million dollar procurement under public scrutiny. The kind of work where a missed deadline or a broken process becomes a visible incident the next morning. That is the operational mindset I bring to debugging your production code.
I have shipped production work with Stripe, n8n, Postgres, Supabase, Claude, and Next.js. I know where each of them breaks, and I know which 5 percent of the codebase causes 80 percent of the incidents. I am not the cheapest. I am the one who delivers exactly what we agreed on.
What I am working on right now.
Making multi-location config updates all-or-nothing
This week I'm deep in the config-governance problem: a business pushes one price, promo, or setting change across many stores, branches, or franchises - and the naive build loops through them, so when it fails halfway, half the locations get the change and half keep the old value, with nothing to say which is which. I'm designing the update layer to be atomic: resolve the full change set first (including locations that inherit from a parent), validate every value, then commit all of them in a single transaction stamped with one batch ID - so the whole set lands or none of it does. I wrote up the full pattern here. Taking on two more fixed-scope projects this month - one week turnaround.