Vertical Slice Migration modernizes a legacy monolith one business capability at a time. Each slice is small, reversible, and proven in production before the next one starts. You get a sequenced plan grounded in your real code, not a big-bang rewrite you bet the business on.
The VSM Command Center runs the method inside your own cloud. Hunter Strategy delivers the migration.
Vera reads the graph and proposes slices. The hairball resolves into named business capabilities, each with real code behind it.
A big-bang rewrite replaces a working system with a promise. Lift-and-shift moves the monolith without making it comprehensible or extractable. Neither reduces the risk that matters. Can you cut a working piece out of a running system? And did you cut the right piece first?
Regulated and security-sensitive programs cannot send source, org models, or telemetry to a commercial SaaS. That one constraint rules out most modernization tooling.
A million-line monolith has no current map. The people who built it moved on. The real seams live only in the code.
Teams rebuild the loud feature, not the used one, because nobody instruments the monolith before they carve it.
When a reviewer asks why the system was carved this way, the answer is tribal memory, not a record.
A vertical slice is one business capability cut top to bottom: entry points, handler code, services, data access, and its own data store. Slices leave the monolith one at a time, following the strangler fig pattern, until the monolith is gone.
Every feature is spread across every layer. Nothing extracts cleanly.
Each slice owns a whole function, top to bottom. It can leave on its own.
Every slice comes from a map of your real code, built before the AI labels anything. If it isn't in your codebase, it can't be in the plan.
The tool stress-tests your real architecture and flags the slices most likely to break on extraction. Surprises happen in the platform, not in production.
Every slice is ranked by how tangled it is and how much data it owns, so your first cut is the cleanest one, not a guess.
Your experts rank priority first, then live traffic re-ranks it. You rebuild the features people use, not the ones that were loudest in a meeting.
Stand new services up around the monolith. Route function to them one slice at a time. Watch the monolith shrink. The chart is driven by real slice status and real traffic attribution, so a program review reads progress off the record instead of a status slide.
No big bang. A sequence of small, reversible cuts, each proven before the next.
The hairball becomes slices on screen. A monolith nobody could read becomes named business capabilities with real code behind each.
The slices to extract first, ranked by how tangled they are and how much data they own, computed from your real code.
How often you ship, how fast, how quickly you recover, how often changes fail. A concrete, measured milestone early on.
The audit trail falls out of the work itself, not a separate documentation effort. Complete by construction.
THE TWO-PASS PRIORITIZATION LOOP
The monolith is dark. Your experts assert what matters. The tool records it as provisional.
The new pipeline goes up and the whole monolith starts reporting usage, before any slice is cut.
The tool infers which slice each request belongs to by tracing it through your code, and flags how sure it is. Priority re-ranks against real traffic.
Once a slice is extracted, it tags its own traffic directly. Estimates become measured fact. Planned versus actual, on one screen.
The team rebuilds the used feature, not the loud one, before they cut a single slice.
A portfolio-grade view of a modernization that used to be a black box, with a decision log that answers "why did we carve it this way" for any reviewer.
The fragility and complexity analysis done for them. A recommended order to move in, with the reasoning shown. Their judgment is the value, not hand-sorting a million-line graph.
A questioning process that respects their time. The priorities they assert get recorded, then confirmed or corrected by real usage.
A board good enough to work in, not around, living in your environment. Every card cites real code. Done always means attributable.
A tool that inherits your boundary, holds no credentials at rest, keeps no source, keeps no raw telemetry, and proves each control with an automated test.
The record is the product. The migration is the proof.
The Command Center makes the method executable at monolith scale, inside your own boundary. One instance ships into one customer's account and inherits its controls. Two agents do the reading. Humans hold every gate.
An agent reads your documents, asks your experts the right questions, and drafts the capability model. It proposes. Your ratifiers decide.
An agent walks the code graph and shapes nominations into a migration plan. Every slice it touches cites real symbols, and the Gate checks every citation.
Vera and Mason return intent. The Gate decides what persists.
The agents can read your system and suggest, but they can't save anything. Every change they propose is checked and passed through the Gate before it is written down. If the AI proposes a slice that points to code your system doesn't actually contain, the Gate rejects it. It is never quietly patched.
The two agents. They read, propose, and explain. They can't save anything themselves.
Clones your repo at a fixed commit, maps how the code connects, then deletes the source. The map stays.
The one place anything gets saved. Every change is validated and logged to a name.
A built-in board that lives in your environment. No Jira, no outside tracker to wire up.
Usage and delivery metrics, tied to each slice. This is what re-ranks priority against reality.
The current phase is a tracked fact, not a status slide. You move forward only through a gate, only by an authorized person, and every move is logged. The same shape repeats four times: the agent proposes, the tool computes, a person ratifies.
Vera reads your org charts, runbooks, and process docs, then interviews your experts to fill the gaps, one question at a time. The result is a clear model of what the business does, versioned, with a confidence level on every item.
Clone your code at a fixed commit. Map it. Delete the source. The tool scores clusters, coupling, and data ownership to rank the safest slices to move first, then proposes candidates one module at a time.
Mason walks the proposed slices in safest-first order and recommends one action each: accept, edit, merge, split, or reject. The call to extract a slice or leave it in place is yours alone.
Engineers work the board in your environment. Standing up the new pipeline is the fixed first job. Every extracted slice tags its own traffic at deploy, checked automatically. Priority re-ranks daily against real usage.
Nothing irreversible happens without a named person and a logged reason.
The AI runs on AWS Bedrock inside your own account. No source, inference, or telemetry leaves your boundary. Every claim below holds only where an automated test or the code itself proves it.
Analyzed in place at a fixed commit, then deleted. What stays is a map and a pointer into your git, never a copy.
Fetched from your secrets store at run time, held in memory for the clone, then dropped. Nothing at rest.
We read aggregate signal tied to each slice, and never copy raw traces out of your monitoring stack.
A named person or a validated agent turn, logged. That log is your audit trail for why the system was carved this way.
The full control matrix, the runtime egress inventory, and the automated test behind each claim are in the security brief.
Replatforming, the platform component, and the Rapid DevSecOps pipeline are Hunter Strategy service delivery, stood up in your CI/CD and cloud. The tool tracks, gates, and proves it. A pilot starts with one monolith, one cloud account, and your boundary intact.
A sequenced, code-cited, production-measured migration plan. Entirely inside your boundary.