Hire AI developers who have already shipped this to production

Hiring for AI is hard in one specific way. The CV that reads strongest is usually the one with the most demos on it, and demos are the part that was never difficult. What decides whether your project reaches production is unglamorous work: permissions, retries, state, rate limits, and reading a result back from the destination before anyone reports success.

Bles Software puts engineers who have shipped that kind of system into your team, on your board, in your repo. It is a way to close a gap, not a replacement for the people you already have, and the fast version of it starts within days rather than after a hiring round.

What you can check about us before you ever talk to us

Bles Software usually ships a first MVP within 32 days of signature and a full deployed version within 59 days. Rates are public rather than quoted on request: $25 to $49 an hour with a $1,000 minimum, listed on our Clutch profile alongside 9 verified client reviews averaging 4.9 out of 5. Founded 2021, Yehud Monosson, Israel.

Those two dates are the ones worth holding a vendor to, because they are the only ones a client feels. Everything before the first MVP is somebody else's process.

What we mean when we say somebody has shipped

An engineer who has run an AI system in production has been burned by things that never appear in a tutorial, and they write the check before the feature.

One of ours, from 28 August 2026. Our own outbound system booked two calls at eleven at night with people who sell exactly the service we sell, because the automation read the message and never the sender's headline. The fix was not a smarter prompt. It was a screen that every caller has to pass, which refuses a supply-side headline before a message can even be drafted. Somebody who has lost a week to that failure writes the screen first.

Another, from 30 August 2026, when we measured our own agent's rule store: 1,175 behaviour rules accumulated, 24 of them actually enforced, and 51 of them minting the same lesson over and over. Safety rules written one day were pushed out of the working set the next. A store that only ever appends is a store that forgets, and knowing that in advance is worth more than knowing three frameworks.

How a hired developer plugs into your team

Week one is deliberately small. Read the code, run it locally, ship one real change that goes to production. It surfaces the friction in your setup while the stakes are still low, and it gives your team something to review rather than a promise to trust.

After that it is your process, not ours. Your board, your repo, your branch conventions, your review, your standup. No parallel tracker, no monthly slide, no black box that reports progress in adjectives.

Overlap hours are agreed before the start, and the overlap we commit to is the one we actually keep. We would rather promise four hours and hold them than promise a full day and drift.

A gap filler, not a replacement for your people

The strongest version of this is your engineers plus one person who has already crossed the bridge you are on. They know the product, the customers and the legacy decisions. The hire brings the specific scars: how agents are supervised, what an evaluation actually needs to measure, why a queue beats a cron, where a model must not be trusted with a rule.

Where an in-house team is the right answer, we say so on the first call. If the software never stops changing and the domain knowledge is the moat, you want that capability permanently in the building, and paying us long term for it is the expensive way to get there.

The work they are good at

Agents that take real actions with supervision and limits. Retrieval over your own documents, with honest evaluation instead of a demo that always uses the same three questions. Integration between models and the systems you run, where the writes have to be safe to retry.

Also the recovery work, which is a large part of what we get called for: a pilot that impressed everyone in March and has not moved since, usually because it was built without state, without limits and without a way to prove what it did.

And the plain engineering underneath all of it. Most AI projects are ordinary software projects with one unusual component, and they fail for ordinary software reasons.

How the engagement starts, and how it ends

It starts with a scoped first slice so both sides can judge the fit on delivered work rather than on interviews. Cost tracks seniority and days per week, agreed in writing before anyone starts, with no recruitment fee.

It ends whenever you want it to, on short notice, with everything in your repo and your infrastructure. Handover is a working session with your engineers plus documentation written for whoever has to fix it at 3am, not a zip file and a farewell email.