Legacy modernization
Keep the old system running while you replace it piece by piece, with no big-bang rewrite.
Strangler-Fig decomposition. Java 1.4 to Java 21 / Spring, phased cutover.
Advisory that ships
Strategic advisory for the systems a business cannot afford to fail: new products, legacy platforms, and the integrations between them, with outcomes you can measure.
The architecture thinking large consultancies bring, sized and priced for the firms they won't take on.
Granular enough for an architect, legible enough for a CFO. Technology-agnostic on purpose: the estate comes first, not a preferred vendor.
Keep the old system running while you replace it piece by piece, with no big-bang rewrite.
Strangler-Fig decomposition. Java 1.4 to Java 21 / Spring, phased cutover.
Stop retyping between systems. Records move on their own, checked and traced.
Batch, MQ, APIs, and file flows between systems never meant to talk.
Hear about a failure before your customers do, and fix it before it costs you.
ELK, Splunk, operational dashboards, and alerting.
Move to the cloud without putting day-to-day operations at risk.
AWS, Azure, GCP with Kubernetes and CI/CD. Roadmaps that protect production.
Make build, buy, and vendor decisions you can defend to your board.
Blueprints, review boards, outsourcing options, build-versus-buy analysis.
See your systems, costs, and risks in plain language before you spend.
Current and target-state diagrams. Cost, risk, and architecture options without the sales fog.
Small budgets. Massive impact
Most AI pilots never move performance because they are bolted on after the architecture is set. We put models inside delivery and recruiting plumbing, with human review on every material decision.
Illustrative model, not a client result.
What that looks like in practice
We spent a month using agentic AI to build the close. Approved hours are read once, the payroll import is written, and the client invoice lines post. The bookkeeper seat is gone, and we run it on Method3 every week.
Auth, APIs, entity search, live job boards, placements, candidates, notes, submissions: the ugly middle of enterprise systems. We wire complex integrations quickly because we read the API and the data model, not only the vendor brochure.
Service porting under architectural review on a mission-critical estate. About 40% faster on the current engagement because the model accelerates the mechanical work and humans hold the line on design.
Azure OpenAI inside the hiring loop: skill extraction, candidate-to-job match support, Boolean and structured search, reply recommendations, outreach drafts. Recruiters approve. No silent rejections off a score.
Every submission retained so drift is measurable: dates that slide, titles that inflate, skills that appear from nowhere. Vector search and compare against prior versions, then hand a clean record into the hiring systems you already run.
Proof at mid-market scale: small teams, real operations, systems that run every day. Click through for the full write-up.
Where most of the estate still lives, and most of the risk
Where most of the estate still lives, and most of the risk
A worked example · vendor governance
You pay a consultancy for the plan, an offshore vendor to build it, and hand over a roadmap to follow. On paper it is clean. Here is how it really goes, and where we change the ending.
The problem
Two-week sprints. Nothing looks wrong until it is structural, and structural is the expensive kind.
Illustrative model, not a client result.
What we do
A neutral set of technical eyes inside delivery. Not a second layer of management, and we do not slow the vendor down.
Embedded as a neutral observer, we spot systemic problems early: misalignment, slippage, designs that will not hold.
We simplify the over-engineered pieces so security and performance actually land, and the offshore team gets clear direction.
Skip the micromanagement. Point the strongest developers at the real goal and let them deliver.
Adjust the design so the team on the ground can actually build it, without dropping quality or the timeline.
The math
Rework is where the money leaks. Catch the drift by Sprint 3 and cumulative rework stays near $225K instead of climbing toward $525K.
Embedded from Sprint 3. Same engagement size, different ending: governance that shows up as cost avoided, not as another status deck.
Illustrative model, not a client result.
On a $1.5M multi-project portfolio
Budget kept out of avoidable rework when oversight starts by Sprint 3.
Fewer rebuilds, fewer “patch it post go-live” traps.
Embedded eyes, not another layer of process on the critical path.
Three fixed-scope ways in. Each one stands on its own and ends with something you keep.
A current and target-state map of your systems, and a prioritized roadmap a CFO can read.
Start here →An embedded review of an in-flight vendor build, with written findings and course corrections.
Start here →Pick one retyping-heavy process. We build it and run it under human review.
Start here →We run these on Method3's operation, then show you systems we actually depend on, not a lab demo.
We spent a month using agentic AI to build it. Approved hours go into payroll and client invoices from one read, with the two sides tied. The bookkeeper seat is gone, and we close Method3 this way every week.
Verification inside the hiring pipeline, not a check bolted on after the offer.
Pipeline health, time-to-fill, and the leading indicators of a vacancy before it arrives.
Bring the legacy estate, the integration nobody owns, or the vendor decision that has to be right. That is the conversation worth having.
Based in Harrisburg. On site when it matters.