Smarter Products
For the CPO, CTO, and CTPO
Accion Labs helps product and engineering leaders build new software, evolve the systems already in production, and modernize the legacy stacks holding the business back, all with governed AI agents. The hard part of enterprise engineering is that every change has to respect the cross-team contracts, architecture, data models, and design rules the system runs on, knowledge that usually lives only in your senior people’s heads. We ground engineering agents in a governed, queryable model of your application, so each agent gets exactly the context a change needs and every change is validated against it. Greenfield, brownfield, and legacy modernization all run on one discipline, Semantic Engineering.
What we deliver
Most product work falls into three situations. We do all three on the same discipline.
New product engineering
When you are building something new, the hard part is fitting it into a landscape it has not been built into yet. We engineer AI-native from day one, design-led, with a model of the application built up as the product grows. You reach the market faster, and the codebase stays coherent instead of accumulating the architectural debt that usually rides along with speed.
Evolving live applications
When the application is already in production, every change has to reason about dependencies across teams without breaking what is live. We run impact-aware change against a model of the running system, so teams move quickly and safely. You get higher feature velocity, fewer regressions, and technical debt held in check rather than compounding.
Legacy modernization
When a legacy stack is costly, risky, and hard to staff, the job is to replace it while honoring years of accumulated behavior. We modernize agentically under a parity contract: the legacy behavior is the contract, the target stack is chosen up front, and agents migrate and validate against both. You get a modern stack with the same behavior, proven by tests rather than asserted.
Related product work runs on the same model: re-engineering and portfolio rationalization, SaaS product development and agentification, and ongoing product managed services.
How we do it
Three things make the opportunity dependable at enterprise scale: a methodology that gives agents the context they need, the platforms that implement it, and an engagement process calibrated to the kind of work.
The methodology: Semantic Engineering
AI coding tools and Spec-Driven Development moved enterprise engineering forward, and within a single team and repository they work well. At enterprise scale, a change also depends on the cross-team contracts, architecture, design system, and data model that live with the people who own them.
Semantic Engineering is how we make that knowledge usable by agents. We encode it as a governed, queryable model of your system. Agents generate only against what the model asserts. Each part of the model has a named human custodian who keeps it honest. Validation gates check every change against the model and produce evidence you can audit. The deep methodology lives on semantic-engineering.ai, and our Semantic Engineering practice shows how Accion applies it.
The shape of the model follows the use case. Building and evolving a live system uses a four-layer ontology of the application. Modernization uses a different shape, sized for a bounded target: the source state, the target state, and a specification that bridges them. Both run on one methodology and the same four custodians.
The platforms that implement it
We implement the methodology through two production platforms, each sized for its use case, both governed by the same model.
Breeze.AI
Continuous SDLC for greenfield and brownfield: the four-layer model, the agent fleet, and the integration surface.
ASIMOV
Agentic legacy modernization, end to end, with functional parity under a parity contract.
Semantic Knowledge Graph
The governed model the agents query and validate every change against.
The engagement process
How we calibrate and engage differs by the kind of work.
Continuous engineering uses the SDLC zones. New build and live-system evolution are continuous work, where the right process depends on the complexity in front of the team. Semantic Engineering describes this as four zones, rising from conversational AI use to portfolio scale. Accion engages where enterprise work actually gets hard.
| Zone | When it fits | What it adds |
|---|---|---|
| 1 · Vibe coding | Throwaway, solo, exploratory | Conversational velocity, no spec gate |
| 2 · Spec-Driven Development | One team, one product, one repository | A written spec the agent generates against |
| 3 · SDD + Semantic Engineering | Multi-team work, or brownfield depth | A four-layer model every change validates against, impact analysis before code, auto-update on every merge |
| 4 · Semantic Engineering at scale | A portfolio of products across years | Multi-product models, a governed agent fleet, custodianship as the operating mode |
We typically engage at Zone 3 and Zone 4, the ranges where AI coding tools and SDD alone run out. The full progression is on semantic-engineering.ai.
Modernization uses five engagement modes. Modernization is bounded delivery, so we calibrate to what you are ready to commit to rather than to work complexity. We enter at the mode that fits where you are, and many engagements progress through them over time.
| Mode | What you get | Typical duration |
|---|---|---|
| 1 · Documentation Only | Functional and technical docs, test scenarios, traceability, read from the legacy code | 2 to 6 weeks |
| 2 · Discovery & Documentation | Inventory, dependency maps, business rules, process flows, SME review packs | 4 to 10 weeks |
| 3 · Migration Readiness | Module-level scope, target mapping, sequencing, risk view, traceability matrix | 6 to 12 weeks |
| 4 · Full Modernization | End-to-end Discover, Document, Migrate, Validate, Maintain, validated against quality gates | Quarters to years |
| 5 · Maintain, Operate & Convergence | Operational knowledge base, change-impact support, convergence blueprint | Continuous after Mode 4 |
You can start by preserving knowledge before SMEs retire, or commit to the full pipeline. The detail per mode is on semantic-engineering.ai.
Proof
In the first sprint under graph-governed UI development, design component reuse reached 53 percent, and an impact analysis agent traversed a 1.6 million line graph in about eight minutes, which is why we partition the graph by product rather than build one monolith across the enterprise.
Named modernizations: ASIMOV Java upgrade, Delphi to .NET, and proprietary-scripting modernization. Product engineering at:
See all Smarter Products case studies.
Who it is for
The strongest fit is mature technology and product firms carrying a meaningful installed base of software, and the PE-backed companies inside them. We do the same work across regulated Healthcare & Life Sciences and BFSI & Insurance. If you build or sell software, the For product companies view goes deeper, and how we engage covers the commercial models.
Modernize with governed agents
From documentation-only through full modernization, on a scope, outcome, or shared-risk basis. Tell us the codebase and the constraint.
Talk to us