We Only Join Two Kinds of Companies.
The company with years of operations and no AI engineers, or the startup with an empty repo. Either way, we become the team you're missing.
30 minutes with the engineers who would do the build. Bring one workflow and you leave with a written one-page read, including when the answer is don't.
Trusted by teams shipping real work
Early-Stage Venture Firm
TV, Film & Podcast Production
Creative & Branding Agency
ScribbleHome Health Documentation Software
PerryAI Pre-Bill Compliance Platform
Enterprise Technology Consulting
Venture Studio & Growth Partner
SyrchAI Investigative Search Platform
Orchid StudioAI Production Assistant for CreatorsMost agents die in one of three places
Not in the build. In the year after it. Every one of these is a gap in the boring parts, and the boring parts are the job.
- 01
The pilot that never left the demo
It looked incredible in the meeting. Then it met real data, real edge cases, and real volume, and it quietly never shipped. The gap between a demo and a system isn't the model, it's everything around it.
- 02
The agent that silently degraded
It worked at launch. Then a prompt drifted, a model was re-versioned underneath it, an input format changed, and accuracy slid for weeks before anyone noticed, usually because a customer did first. Nothing was watching.
- 03
The vendor who left at handoff
You were handed a repo nobody on your team can operate, no evals to trust a change against, and no way to tell whether tomorrow's edit made it better or worse. So it froze, because touching it felt riskier than leaving it.
None of these are model problems. They're engineering problems, and they're the ones we take responsibility for.
What You're Actually Hiring
Most companies don't need another AI vendor, and most founders don't need another agency. Years of operations with no AI engineers, or a company name and an empty repo: both need engineers who take responsibility for the software actually working, and who show up every week until it does.
That's the job. Where there's already an engineering team, we join it and own the AI side: what to build, how to build it, and making sure it ships. Where there isn't one yet, we are the engineering team, and it's the same three things. Either way we staff small and senior, two or three engineers per engagement, and we're full-stack, so the rest of the product is in scope, not handed off.
Own the Roadmap
We audit where AI genuinely fits your operations, and where it doesn't
We prioritize by impact and feasibility, not hype
We kill dead-end ideas before they burn a quarter
Architect the System
Model selection, RAG vs. fine-tuning, build vs. buy calls
Agent design: tools, memory, orchestration, human-in-the-loop
Cost and latency budgets set before code is written, not after the invoice
Build Hands-On
We write the code: agents, APIs, frontends, and infra, from prototype through production
Working software every week, not slide decks
Your repos, your cloud, your standards
Multiply Your Team
Eval harnesses and agent patterns your engineers can extend
Code review and pairing on AI features
The goal is a team that ships AI without us, not a permanent dependency
Worth saying plainly: everyone here is senior, and there are not many of us. The engineers on the intro call are the ones writing the code.
Agents Inside a Business That Already Runs
This section is for the company that already has customers and a payroll. Our flagship work runs inside the business: the queues, handoffs, and back-office processes your customers never see, and where the cost actually sits. Agents that take real actions in real workflows, backed by the evals, guardrails, and monitoring that separate a demo from a system you can trust. If your repo is still empty, skip ahead one section.
Support & Triage Agents
Read inbound tickets and emails, classify intent, draft responses from your docs and past resolutions, resolve the routine ones, and escalate the rest to a human with full context attached.
Document & Back-Office Agents
Intake, extract, validate, and route: invoices, contracts, forms, and applications turned into structured data in your systems of record, with a human approval step where the stakes demand it.
Internal Knowledge Assistants
Permissions-aware retrieval over your wikis, docs, and past tickets, with sources cited on every answer, so "ask the bot" beats "ask around and wait."
Workflow & Ops Agents
Multi-step processes across your CRM, email, and ticketing via tool calls: drafting, updating, scheduling, and reconciling, with approval gates on anything irreversible.
Data Agents
Enrichment, deduplication, recurring reports, and monitoring that watches your data and speaks up when something looks wrong, instead of a dashboard nobody opens.
Browser & Computer Use Agents
Agents that operate software directly: navigating web apps, filling and submitting forms, and pulling data out of the systems that never shipped an API. For the internal tools built long before anyone planned to integrate with them.
What "production" means here: an eval suite built from your real cases, guardrails and fallbacks for when the model is wrong, tracing on every run, cost monitoring, and documentation your team can operate from. If it can't be measured and trusted, it doesn't ship.
We ship user-facing agents and whole agentic products too. But every card above assumes a business already running around it. If yours doesn't exist yet, the next section is the one written for you.
Workflow not on the list? That's what the intro call is for. And if an agent is the wrong tool, we'll say so on the call, for free.
Your First Engineers, Before Your First Hire
The other company has no operations to automate yet. It has a founder and an empty repo. We join as the first engineers and build the product itself, agents at the core: from nothing to an MVP real users touch, then toward product-market fit. The same two or three senior engineers, with no junior bench behind us to hand it to.
The Whole Build, Not Just the AI Part
Auth, data model, deploys, the frontend users actually touch, and the agents underneath it. We build the product, not the AI part of somebody else's product. There's nobody to hand the rest to, which is the point of staffing full-stack.
Agents at the Core, Not Bolted On
If the agent is the product, how it behaves is a product decision, not an integration task. We design the loop, the tools it can call, and the eval suite that says whether it's working.
Evals Before You Have Volume
You won't have a year of tickets to test against. We build the eval suite from the cases you can describe and the ones that worry you, then grow it as real usage arrives. It's how you find out the agent is wrong before a customer does.
Yours to Take Over
Your repo, your cloud, your accounts, from the first commit. When you hire your own engineers, they inherit a codebase with evals, tracing, and docs, not a black box only we can touch. We build for that handoff from the first week, not the last one.
We take a small number of these at a time, and a build is two or three people, so the fit has to be real. Sometimes the honest answer is that what you need isn't engineers yet, it's more customer conversations. We'd rather say that on the call than three months into the build.
How an Agent Ships
Same five stages either way. With a business behind you, stage one is one existing workflow, proven on your real data before it touches production. With an empty repo, it's the first job the product has to do for a user, and the real data is whatever you can get your hands on in week one. No big-bang projects in either version.
1. Scope
Typical duration: 2 weeks · 8–12 stakeholder interviews
Pick one workflow with clear value, or from an empty repo, the first job the product has to do
Define what "correct" looks like in numbers, plus a cost and latency budget
Audit the data and tools the agent needs
Deliverable: a written build plan with architecture and cost estimate
2. Prototype
Typical duration: 2–3 weeks on your real data
A working agent on your real data in weeks, not quarters
Ugly on purpose: the point is proving the core loop works
Deliverable: a demo you can put in front of the people who'd use it
3. Evaluate
Typical duration: 1 week
An eval suite built from your real cases and hard edge cases, run on every change
Measure accuracy, cost per run, and failure modes
An honest go/no-go, including "don't build this" when the numbers say so
Deliverable: an eval report you can make a decision on
4. Production
Typical duration: 2–4 weeks · staged rollout
Guardrails, fallbacks, and human approval gates
Staged rollout: shadow mode first, then supervised, then autonomous where the numbers justify it
Tracing, alerting, and cost controls, deployed in your cloud, in your repo
Deliverable: a live agent your team owns
5. Operate
Typical duration: Ongoing · runbook at handoff
Review transcripts, catch drift, tune prompts and models as they change
Runbook and handoff docs so operating it doesn't require us
Deliverable: a system that keeps working after we step back
Nolan Makatche
Founder & Principal Engineer
Building agents since before they were called agents, starting in 2022, and working in AI full-time ever since. Has shipped agents across healthcare, legal, fintech, insurance, media, marketing and B2C, and contributes to ODS, a local AI server with nearly 4,000 stars. Technical.ly named him a RealLIST Innovator in 2025. The work is public: GitHub and the full résumé.
Nolan co-founded Coffee & Code Philly, a network of nearly 5,000 engineers and founders, and Philly Tech Entrepreneurs, a network of nearly 2,000 founders. So when a project needs a second or third engineer, we don't post a job. We call someone onshore and senior we have already worked alongside.
No account managers, no junior hand-off. The person who scopes the work on the call is the person who ships it.
Local LLM Deployments
Some of the best agent use cases sit on data that legal, security, or a customer contract won't let you send to a model provider. For those we run the entire system on infrastructure you control: the model itself runs on your servers or in your own cloud account, with no outbound calls to anyone's API. We use open-weight models, which means the weights live on your disk instead of behind someone else's endpoint. That's a real engineering constraint to design around, not a checkbox on a questionnaire.
Nothing Leaves
Prompts, documents, customer records, and PHI stay inside your network boundary, because there is no outbound call for them to leave through. That closes the third-party data-sharing question. Your egress rules, logging, and host access are still yours to set.
Air-Gapped If Required
Where the environment has no internet egress, the models, retrieval, and orchestration are built to run inside it. Updates land on your schedule, through your change process, not when a vendor decides to ship.
The Model Doesn't Move
A hosted model can be deprecated, re-versioned, or re-priced underneath a system that was working fine, and you find out from a changelog. Weights you host stay exactly as evaluated until you decide otherwise.
Your Bill, Your Uptime
You're paying for servers instead of per token, and you're not subject to someone else's rate limits or status page. The honest cost of that: capacity planning is now your problem, and unused hardware still bills.
Built for regulated environments
We build agents for HIPAA-regulated workflows and for teams operating under SOC 2. What the agent may access, who approves what, what gets logged, and how long data is kept are decided before we write code, rather than reconstructed for a questionnaire six months later. Retrieval is permissions-aware, so the agent inherits the asker's access rather than the whole corpus, and anything irreversible waits on a person. We deploy in your cloud and your repo. That's slower than pointing a prototype at a hosted API, and it's the only version that survives a security review. The certification is yours to hold, not ours; our job is making sure there's something real underneath it when your auditors ask.
Ways to Work Together
AI Roadmap Sprint
2–3 weeks · fixed scope · credited toward your first build
A short, fixed engagement: we audit your operations, data, and team; identify the highest-leverage agent opportunities; and leave you with a prioritized roadmap and architecture recommendations. Useful on its own, and the natural first step to everything below.
- Core Offer
Agent Build
6–10 weeks · one scoped agent, end to end
The core offer. One scoped internal agent, end to end: scope, prototype, evaluate, production, operate. Two or three senior engineers own it from empty repo to a live system your team runs, with a defined handoff at the finish.
Embedded AI Team
Monthly · a set number of days, ongoing
A set number of days per week or month, ongoing. We own AI delivery, build agents through the process above, make the architecture calls, and level up your engineers as we go. Senior direction without the recruiting cycle.
Founding Engineering Team
Ongoing · scoped to your stage
For zero-to-one teams. We join as your first engineers and build the whole product, agents at the core: from empty repo to MVP, then toward product-market fit. Full stack, shaped to the stage.
This is for you if:
You have real internal workflows an agent could take over, but no senior AI engineers to lead the work
You're a founder who needs agents shipped, not another strategy deck
Your engineers are strong but new to LLM systems, and you want the capability in-house after we're gone
You're pre-product and need a team that can take the whole build from zero to MVP, agents included
It's not for you if you want someone to validate a decision you've already made, or AI on the roadmap for the optics rather than the work. If the fit isn't there, we'll say so early and point you somewhere better.
The next step is a 30-minute call: bring one workflow or a whole product idea, and you leave with a written one-page read on whether and how we'd build it.