AI DevAgentic systems let a small team ship enterprise-level applications without sacrificing code quality — through an AI-run SDLC, efficient processes, and relentless optimization.
Follow the lifecycle top to bottom — each phase runs on agents, with senior engineers in control. Open any phase to see how (and why) we automate it.
Turn vague asks into a precise, testable spec.
A requirements agent interviews stakeholders, drafts user stories, and surfaces ambiguity before a line of code is written.
Design systems that scale — and stay clean as they grow.
An architecture agent proposes options, weighs trade-offs against your constraints, and records the decision as an ADR.
Break the work into a sequenced, estimated, parallel-ready plan.
A planning agent decomposes the spec into tasks, estimates them, maps dependencies, and identifies what can run in parallel.
Write production code fast — to the standard you set.
Coding agents implement tasks against the spec and your conventions, while engineers steer architecture and review every change.
Catch issues on every PR — consistently, instantly.
A review agent checks every pull request for bugs, security, and standards before a human ever opens it.
Generate tests, run regressions, and prove it works.
A QA agent writes unit and end-to-end tests, runs the suite on every change, and triages failures with a likely cause.
Ship safely, on every merge, with a way back.
A release agent manages CI/CD, runs pre-flight checks, ships behind flags, and watches the rollout for regressions.
Docs that stay current — and a system that stays healthy.
A docs agent keeps documentation in sync with the code and watches the running system for issues to fix.
The point of an AI-run SDLC isn't speed for its own sake — it's hitting an enterprise quality bar that small teams normally can't.
3.5×
Faster delivery
idea → production vs a traditional team
68%
Less time in review
agents clear the routine first pass
40%
Fewer escaped defects
caught by evals before release
90%+
Test coverage
generated and kept current by agents
Representative figures from our AI-SDLC engagements.
What changed when these organizations moved from a manual lifecycle to an agent-run one — structure, and the metrics that followed.
Swapped a stalled offshore team for a senior squad running the AI SDLC.
Dev time
6 mo 6 wks
Code grade
C A
Deploys
1 / wk 12 / day
Adopted the pipeline to clear a growing backlog without growing headcount.
Throughput
1× 3.5×
Escaped bugs
High -40%
Coverage
45% 92%
Rebuilt a legacy module to enterprise standards with a small core team.
Lead time
Weeks Hours
Incidents
Frequent Rare
Onboarding
4 wks 3 days
Illustrative examples based on typical AI-SDLC engagements.
AI handles the slow parts; your engineers stay in control.
Boilerplate, tests, and migrations — AI-assisted.
AI drafts, seniors review, evals catch regressions.
Engineers on architecture, not glue code.
More output from the same team.
Map your pipeline; find where AI pays off.
AI coding, review & test tooling in your stack.
Playbooks, guardrails, hands-on training.
Track quality & velocity, then roll out.
We build AI with AI. Our own tooling lets us prototype, evaluate, and harden intelligent features far faster than traditional development.
Anyone can build a flashy demo. We ship AI that behaves predictably, with the measurement to prove it.
We define the use case, success metrics, and a realistic plan — including where AI is (and isn't) the right tool.
We build fast, then measure quality with evals on your real data before going further.
We add guardrails, cost controls, and monitoring, then integrate the feature into your product.
A couple of quick questions so we can send a proposal that actually fits how your team ships.
Retrieval grounds answers in your approved sources, guardrails block ungrounded or out-of-scope responses, and an evaluation suite catches regressions before users do. Every claim can be cited.
We assess your current pipeline, set up AI coding/review/testing tooling inside your stack, train your engineers with guardrails and playbooks, then measure quality and velocity before scaling it across teams.
No — it frees them. AI removes the slow, repetitive parts so your senior people spend their time on architecture, product, and the hard problems.
Cost controls, caching, and graceful fallbacks are built in, with full observability so you can see exactly what the AI did and why.
Book a free intro call or send us your project. We'll show you exactly how we can help — and how fast.