AI-native software delivery

In partnership with OpenAI Select Partner

High velocity.
Production discipline.

I lead a four-person software team where coding agents work in parallel across real products. Engineers set direction, architecture and acceptance criteria; agents research, implement, test and iterate. The result is a much shorter path from requirement to verified change, without lowering the bar for delivery.

See the operating model

Kamil Biduś · Founder and technical lead at Syntropic

The AI-native operating model

One engineer no longer means one stream of work.

The bottleneck moves from writing every line to framing the right work, supplying context and verifying what comes back. Parallel execution creates speed. Closed feedback loops keep that speed useful.

01

Frame the outcome

A senior engineer defines the intent, constraints, risks and evidence that will prove the change works.

02

Split the work

Independent tracks run in isolated checkouts, so research, implementation and review can move at the same time.

03

Close the loops

Agents run builds, tests and browser checks, inspect failures and iterate before the work reaches human review.

04

Accept in reality

A person validates the working change, owns the trade-offs and controls what is allowed to reach production.

Velocity Parallel work
Short feedback loops
Quality Executable checks
Human acceptance
Outcome More verified change
per engineer

The always-on maintenance layer

Maintenance becomes a running system.

Routines turn recurring engineering work into infrastructure. Each one has a narrow job, runs on a schedule, verifies against the real system and opens a reviewable change. The engineer stays responsible for what lands.

01

Reliability

Recurring
  • /crash-fuzzer

    Exercises the real application and opens root-cause fixes.

  • /logic-bugfixer

    Models difficult logic, finds gaps and verifies edge cases.

  • /flaky-test-fixer

    Reproduces intermittent failures and repairs the cause.

02

Codebase health

Recurring
  • /logic-simplifier

    Reduces convoluted business logic without changing behavior.

  • /dup-unifier

    Finds parallel implementations and brings them back to one.

  • /dead-code-removal

    Removes code only after evidence shows it is unused.

  • /useless-test-pruner

    Deletes tests that cannot fail or protect no behavior.

03

System integrity

Recurring
  • /feature-flag-inliner

    Removes completed flags and the obsolete paths behind them.

  • /abstraction-improver

    Flattens abstractions that add cost without leverage.

  • /abstraction-police

    Finds layering violations and restores architectural boundaries.

01

Scheduled

Runs without a fresh prompt, against a narrow recurring job.

02

Verified

Uses the real system and attaches reproducible evidence.

03

Reviewable

Opens a proposed change; automation never approves itself.

04

Compounding

A rejected result improves the routine, not only the PR.

From delivery to environments

Work that tests the whole engineering loop.

Our environments are grounded in the work agents encounter inside a real delivery system, not in isolated code generation.

01

Software engineering

Agents navigate a real codebase to change distributed workflows, migrations, concurrency and recovery paths without breaking behavior across service boundaries.

stateretriesmigrations
02

DevOps

Agents diagnose CI and delivery failures, change versioned infrastructure and prove that systems still build, deploy and remain operable.

CI/CDIaCoperability
03

Data engineering

Agents build or repair initial, batch and real-time loads where consistency, idempotency, indexes and database coordination determine correctness.

PostgreSQLpipelinesconsistency

Production background

Engineering behind the task.

The model is grounded in systems we have engineered and software we continue to deliver.

01 / Regulated systems Team delivery

Distributed vector storage in a major bank

As a tech lead at Citi, Kamil worked with a team that built a distributed vector storage system on PostgreSQL and pgvector for regulatory use. The engineering covered distributed transactions, idempotent processing, saga design, a transactional outbox, initial, batch and real-time ingestion, indexing and advisory locks.

Useful task material: state, ordering, failure recovery and correctness across service boundaries.

02 / Production SaaS Founder-led

A four-person AI-native delivery team

At Syntropic, product work is framed as reviewable outcomes and distributed across engineers and coding agents. Agents explore the codebase, implement changes and run verification in parallel. The team resolves ambiguity, reviews the evidence, accepts changes in a running application and controls the release.

Useful task material: incomplete requirements, parallel workstreams, integration boundaries, regressions and operational acceptance.

Quality at AI speed

Velocity compounds only when verification does.

Every workstream carries its own evidence. Automated checks shorten the loop; human acceptance remains the final authority for user-facing changes.

01

Verification stays inside the loop

Agents do not stop at generated code. They run the build, tests and browser checks, inspect failures and iterate until the change produces reviewable evidence.

02

Review covers behavior, not only code

AI proposes acceptance scenarios from the specification and the actual diff. A person exercises them in the running application and checks what changed, what should work and what may have regressed.

03

The delivery path remains controlled

PR, review, build, versioned configuration, infrastructure as code and controlled deployment remain enforceable constraints. A missed regression becomes a better check, instruction or monitor.

01

Scope

Agree the task family, boundary conditions and what a correct result must prove.

02

Build

Create the runnable environment, reset path, task variants and grader.

03

Challenge

Test for shortcuts, leaked answers, false positives and brittle setup.

04

Handoff

Deliver the environment with evidence, acceptance notes and known limitations.

Kamil Biduś KB / Syntropic

One accountable lead

Built with the team.
Owned by the founder.

I am Kamil Biduś, founder and technical lead at Syntropic, the operating brand of VECTORLENS Sp. z o.o. My career began with internships at Amazon and Goldman Sachs, followed by two years at Sumo Logic. I then spent three years at Citi, progressing into a tech lead role before founding Syntropic.

I scope the work, stay responsible for technical quality and remain the direct point of contact through delivery.

Sumo LogicCitiAmazonGoldman Sachs

Direct contact

Open to discussing the work.

If our production background is relevant to the environments you are building, Kamil is the direct point of contact.