The governance layer for AI-driven software delivery.

2–4×faster change cycle
one graphevery phase sees the whole system
100%of changes carry approval
up frontimpact analysis before the change
lower costper validated change
full controlover every agent change
Load your system into POLYREQ → How it works
What you get with us

We take your delivery from SDLC to ADLC.

In the Agentic Development Life Cycle, AI agents deliver changes with full context of your system. POLYREQ makes your system ready for it.

Today
SDLC
Software Development Life Cycle
  • System knowledge lives in people’s heads and stale documents.
  • Impact analysis is manual and comes out different every time.
  • AI agents guess context from code. The same question gives two different answers.
  • What’s deployed slowly drifts away from what was approved.
POLYREQ
With POLYREQ
ADLC
Agentic Development Life Cycle
  • A living knowledge graph of your system with evidence in code.
  • A complete impact map for every change, with the same result every time.
  • AI agents get precise context via MCP and deliver against it.
  • Approved is automatically compared with deployed. No silent drift.
01

Load your system

Your codebase and documentation go into POLYREQ: legacy, supplier-built and AI-written systems alike.

02

Context becomes AI-agent ready

POLYREQ builds the typed model: processes, rules, screens, endpoints and data, every record with evidence in code.

03

Delivery goes agentic

Your delivery moves to ADLC: changes are shipped by AI agents with precise context and full traceability.

Get your system ready for ADLC →
End-to-end

From business need to production.

AI agents run every phase, not just the coding. POLYREQ is the ground truth they read for context and write their results back into, so the model stays live and nothing drifts from need to production.

The SDLCWaterfall, Agile or DevOps · disconnected tools, context lost between phases
ADLCthe same lifecycle · now run by AI agents

Business discovery

Business talks through its requirements with AI agents, plainly.

Impact + prototype

Every affected part is mapped, then shown in a clickable prototype.

v3

Technical specification

The validated change is written up as versioned technical specs.

Agentic
delivery

Coding agents build the software via MCP, with humans approving.

Automated verification

Each build is checked against the approved spec, automatically.

Controlled
release

Shipped to production, with approved-vs-deployed confirmed.

Traceability & audit

A full trail from idea to production, auditable at any time.

One living specs graph the shared context every phase reads and writes
Vendor-neutral

Works with any AI agent.

Governed & secure

Enterprise governance at every step.

Cost-optimized

Tasks routed to the best-value model.

Collaborative

Built for whole teams, not just IT.

Scalable

From monoliths to microservices.

Why now

Software now grows faster than anyone can follow.

AI writes code faster than ever. The teams that stay ahead are the ones that still know what it does: which rules run their business, where they live, and what every change affects.

0 %
of developers already use AI at work
0 %
have little or no trust in AI-generated code
0 %
have discovered AI-related errors after deployment

Source: DORA 2025 · State of AI-assisted Software Development

Your system today

Hard to keep track

Years of changes, departed authors, AI-written code. The system runs your business, but nobody knows exactly what’s inside anymore.

01 · Understanding

Understand it

POLYREQ walks through code and documentation and builds a knowledge graph: processes, rules, screens, endpoints, data. Every record with evidence of where it lives in the code.

System understanding →
02 · Changes

Steer it

A change request is evaluated against the whole model: affected records with evidence, untouched ones with a reason. Collisions between two changes are visible instantly.

How the platform works →
03 · The outcome

Deliver without surprises

Two changes touching the same record? You see the collision at analysis time, not in production. And approved is automatically compared with deployed, no silent drift, no hidden tech debt.

Why agents alone can’t →
For business people

Change the app with a sentence. The specification writes itself.

Business doesn’t need to read code or file tickets. They get a clickable prototype, change it with a prompt, and every change is rewritten into specification records in the background.

Contracts overview + New contract
Search Filter: Region ▾ Status ▾
Prototype feedback ● live
Business · Product owner “Add a filter by region to the overview.”
Screen Contracts overview v3 → v4 ✎
Rule Region filter new record ✎
Use case Find a contract unchanged · reason

Prototype by prompt

Say what to change. The prototype changes and the specification updates in the background, before the first line of production code.

Your concepts, not tickets

Rules, requirements and use cases linked all the way to code, readable without an IT dictionary. Business works with what it understands.

Every input leaves a trace

No decisions lost in emails and meeting notes. Every comment is a versioned change to a record, traceable even a year later.

What the model watches over

One map of your system. Every answer.

Change A Change B Data ⚠
Collisions

Two changes, one record

Parallel change requests touching the same record are caught by overlapping impact maps at analysis time. Not in production.

APPROVED DEPLOYED =
Drift

Approved = deployed

The comparison runs automatically over linked records. Every difference is a tracked finding with an owner. No silent deviation.

Use case v1 v2 v3
Identity

Approvals survive rewrites

An approved use case stays the same even after ten AI rewrites. You approve a versioned record with history, not a paragraph of text.

Rule Screen 1 Screen 2 Screen 3 Screen 4
Links

“Which screens use this rule?”

An instant, complete answer from the links between records. No re-searching the whole system for every question.

Coverage

Nothing slips through silently

Every part of the system evaluated: affected with evidence in code, or untouched with a reason. “Nothing else changes” can be verified.

MCP AI AI AI
Agents

Precise context via MCP

Coding agents pull exactly the records and links the task needs. Precise input, precise output.

POLYREQ

Real impact for business and engineering.

70 %
faster impact analysis

Analysis that took days becomes a matter of hours on top of the finished model. And manual analysis work drops by 40 to 60 %.

50 %
less on tokens for AI tasks
60 %
fewer AI hallucinations
2–4×
faster change request cycle
30–50 %
higher success rate delivering changes

Results vary by organization and use case.

Show me on our system →

Your AI is only as good as its context. Via MCP it reads precise records and links straight from POLYREQ. We make agents better, not obsolete.

Where to start

Pick the situation you’re in today.

Load your system into POLYREQ POLYREQ understands it, makes it AI-ready, and runs governed change over it from day one. Legacy, supplier-built or inherited code, in days. System understanding “We ship with AI and want control.” A living model, impact maps, collisions and approved-vs-deployed, for teams that ship fast and intend to stay in command. The platform for teams “We’re building something new.” From an idea (or from a knowledge graph of the old system) to a specification, a clickable prototype in 48 hours, and delivery at a transparent price. Custom development