Staff augmentation with senior engineers who join and stay
You interview every one of them before they start. They work in your repository, under your review process, and they stay on after launch.
What you are buying
4 things that decide this
- 01You interview every engineer before they join your team, and you can decline any of them.
- 02They work in your repository, your standups and your review process. Your engineers approve their pull requests.
- 03Senior only. We do not put a junior on your project to fill a seat.
- 04They stay after launch under an agreed service level, or they train your team to take it over.
Most staffing goes wrong at the same two points
The first failure is who arrives. A profile gets approved, a different person joins the standup, and by the time you notice, two sprints are gone. The fix is boring and it works: you meet the engineer first.
The second failure is what happens at the end. The contract closes, the engineer moves on, and the thing they built has nobody who understands it. AI work makes this sharper. Every model behind a production system gets retired eventually, and a team that has already left cannot tell you whether the replacement still passes.
- 01Your review process stays yours. Nothing merges because an outside lead approved it.
- 02Code sits in your repository from the first commit, not in ours until final payment.
- 03One engineer, or a group. You set the priorities either way.
- 04Ending it costs nothing extra. No penalty clause for deciding the fit is wrong.
What we staff
Every role here is filled by a senior engineer who has shipped the thing in production, not studied it.
AI and agent engineers
People who have built systems that call tools and change state, and who know what to do when a step fails halfway through.
Hire AI developers →
Claude and LLM engineers
Engineers who have taken the Claude API and MCP into production, including the evaluation suite underneath.
Hire Claude developers →
Retrieval engineers
Chunking that survives a scanned PDF, permissioned retrieval, and evals that catch quality drift before a customer reports it.
Hire RAG engineers →
AWS developers
Engineers who have run multi-tenant platforms and IoT pipelines on AWS in production, and design for the bill as well as the uptime.
Hire AWS developers →
Product engineers
React, Python and mobile. An AI feature still needs an interface and a backend that hold up under real traffic.
- ScopeFree call. What you need, and who fits.
- InterviewYou meet them and can decline.
- EmbedYour repo, your standups, your reviews.
- ShipPull requests your team approves.
- StayAgreed service level, or trained handover.
The interview step is the one a marketplace cannot offer you. A profile and a rating are not the same as meeting the person who will write the code.
The question a rate card cannot answer
Ask anyone you are about to hire what happens when the model your system runs on is deprecated. Prompts behave differently across model versions. A team that has not planned for it will find out under pressure, with your users watching.
The answer is an evaluation suite. Cases drawn from real failures, run against the new model, so the swap becomes a decision instead of a gamble. Our engineers build that alongside the feature, because retrofitting it after an incident is how teams end up rewriting.
- Evals written from the failures your system actually had.
- A runbook your team can follow without calling us.
- Monitoring on cost, latency and answer quality, from day one rather than after the first incident.
The stack
AI
Backend
Frontend and mobile
Run
“They will treat your vision like their own and build it that way.”
Ron Klabunde · Founder, SmartREI ↗
A marketplace against an embedded engineer
Who you meet
Marketplace or job board
A profile, a rating and a filter percentage.
Embedded with us
The engineer, in an interview you run. Decline anyone.
Where the code lives
Marketplace or job board
Sometimes theirs until the invoice clears.
Embedded with us
Your repository from the first commit.
Who reviews the work
Marketplace or job board
Nobody, or a delivery manager you never meet.
Embedded with us
Your engineers, in your pull request process.
Evidence they can do it
Marketplace or job board
Hours billed on the platform.
Embedded with us
Named systems in production, with the mechanism described.
When the model is deprecated
Marketplace or job board
The engagement ended. Your problem.
Embedded with us
An eval suite that tells you whether the swap is safe.
After launch
Marketplace or job board
The contract closes.
Embedded with us
An agreed service level, or your team trained to run it.
Questions, answered
01What is IT staff augmentation?
IT staff augmentation adds outside engineers to a team you still run, instead of handing a project to a vendor to deliver. You keep the roadmap, the review process and the definition of done. At Hashlogics every augmented engineer is senior, works in your repository, and is interviewed by you before they start.
02Do we interview the engineers ourselves?
Yes, and you can decline any of them. You meet the specific person who would do the work, not a representative profile. Any vendor who resists this is telling you something.
03How is this different from outsourcing a project?
Staff augmentation gives you people; outsourcing gives you an outcome. With augmentation you set priorities sprint by sprint and your team reviews the code. Outsourcing suits a bounded piece of work you would rather not manage at all. The first 2 months of support and maintenance are free, with every build.
04What if an engineer is not the right fit?
Tell us and we replace them. The interview exists to make that unlikely. But a bad match is our problem to fix, not something you are locked into for the rest of a contract.
05Who owns the code they write?
You do, completely, from the first commit. Source code, prompts, models and data pipelines are yours. Nothing is licensed back to us and nothing depends on us to keep running.
06Which time zones do they work?
You get at least four hours of overlap with your working day, agreed before anyone starts. Most clients take a longer overlap so standups and code review happen live rather than overnight.
07Is this only for larger teams, or does it work for an early-stage startup too?
Both, and the fit is different. An enterprise usually needs the engineer to work inside existing permissions, audit requirements and a legacy stack from day one. A startup usually needs one feature shipped and evidence it holds up before the next round of funding gets raised. Same interview step, same review process, either way.
08What does it cost?
Scoping is free, and you get a written scope before you commit to anything. Where we have to go into an existing codebase first, a paid two-week diagnostic sets a fixed price for the work that follows. We are not the cheapest bid and do not try to be.
09Can you cover both AI and normal product work?
Yes, and most engagements need both. An agent that reads your documents still needs a working interface, a backend that holds up, and someone to keep both running after the launch.
Related
- Agency vs in-house AI team →When hiring your own people is the better call.
- Staff augmentation vs outsourcing →People you direct, or an outcome you buy.
- Hire Claude developers →Engineers who have shipped Claude to production.
- Best staff augmentation companies →The signals that separate real augmentation from a bait-and-switch, applied to us too.
- AI development services →The work most augmented engineers join to do.
