Legacy assessment and modernization map
We read the code, interfaces, data model and job schedules, then interview the people who run the system. You get a module-by-module map scored by business value and risk, with a recommended order of work.
Integration and modernization pod
Modernize legacy systems piece by piece,
Modernize legacy systems piece by piece,
not in one rewrite
An integration and legacy modernization pod is 3–5 tkxel engineers who connect your ERP, CRM and legacy systems through APIs and events, then replace the riskiest parts one module at a time while the old system keeps running.
Book discovery call
Our clients
KnowlesSterne KesslerRepleniumABBClient fiveClient sixClient sevenClient eight
What is your legacy system
costing you each quarter?
Your AI plans stop at the ERP
The data and actions your AI features need sit behind an old ERP or mainframe with no usable API. Every new idea starts with a custom extract.
Changes take months and few people dare
The code is undocumented and the people who wrote it have moved on. Small changes need long test cycles because nobody knows what else breaks.
Integrations are a web of scripts
Nightly file drops, database triggers and one-off scripts move data between systems. When one fails, records drift apart and someone fixes them by hand.
A full rewrite feels too risky to fund
A multi-year replacement freezes new features and puts the business on the line at cutover. So the old system stays, and the workarounds grow.
What the pod delivers
across your legacy estate
API layer around legacy systems
We put documented REST or GraphQL APIs in front of legacy functions and data, with authentication, rate limits and versioning. New apps and AI features call the API instead of reaching into the old database directly.
ERP and CRM connectors
We build and maintain connectors for platforms such as SAP, Oracle, Microsoft Dynamics, NetSuite and Salesforce using their supported APIs and integration tools. Mappings, error queues and replay handling keep records consistent across systems.
Event streaming and integration layer
We replace brittle point-to-point scripts with an integration layer using message queues, change data capture or an iPaaS your team already licenses. Systems publish changes once, and each consumer subscribes without new custom code.
Strangler fig module replacement
We route one capability at a time from the legacy system to a new service behind a facade, starting with lower-risk modules. Both run in parallel until results match, then traffic moves over and the old path retires.
Anti-corruption layer for business rules
Where legacy logic is wrong or tangled, we isolate it behind a translation layer instead of wrapping it as is. New services work with clean models while we document and test the rules being carried forward.
Data migration and reconciliation
We migrate data in stages, with automated reconciliation reports comparing record counts, totals and key fields between old and new systems. Each cutover has rehearsals, a go/no-go checklist and a tested rollback.
Want to see which module can move first?
Tell us which system holds you back, and we’ll sketch a first module to modernize.
Plan your first moduleHow the pod modernizes
without stopping your business
-
We assess code, interfaces, data and operations for the systems in scope. Together we choose the first module or integration by value and risk.
Deliverables: Modernization map, integration inventory, risk register, first-module plan
-
We build the API or event layer around the legacy system so new work stops touching it directly. Existing users notice no change.
Deliverables: API layer, connectors, integration tests, monitoring
-
We build the new service, run it in parallel and reconcile results against the legacy path. Traffic moves over in stages once outputs match.
Deliverables: New service, reconciliation reports, cutover runbook, rollback plan
-
Your team takes ownership of the new service and integration layer. We agree on the next module and whether your team or a pod leads it.
Deliverables: Handover sessions, architecture records, updated map, next-module backlog
What incremental modernization
helps you achieve
Give AI features access to core systems
A documented API layer exposes ERP and legacy data and actions without a custom extract for each new use.
Keep the business running during change
Parallel runs and staged cutovers keep the legacy system live until each new module proves itself.
Retire risk one module at a time
The strangler fig approach replaces the most fragile parts first, so risk drops with each release rather than at one cutover.
Cut manual reconciliation work
An event-driven integration layer with error queues and replay keeps records in step across ERP, CRM and other systems.
Spread modernization spend across releases
Each module is a separately scoped outcome, so you fund and review progress in stages instead of one large program.
Ready to see this in your own workflows?
Book discovery callRewrite, wrap or modernize
incrementally: how they compare
All three can work; they differ in when risk lands, how cost is spread and what runs along the way.
| Criterion | Full rewrite program | Integration wrapper only | tkxel integration and modernization pod |
|---|---|---|---|
| Approach | Replace everything at once | APIs over existing system | APIs first, then module replacement |
| Value delivered | At final cutover | As soon as APIs ship | With each module released |
| Business disruption | High at cutover | Low | Low, with staged cutovers |
| Legacy logic problems | Fixed if rediscovered correctly | Carried forward unchanged | Isolated, then replaced |
| Total duration | Often multi-year | Short | Ongoing, module by module |
| Fails when | Requirements change mid-program | Underlying logic is broken | Parallel runs skipped under deadline |
Featured success stories
[Placeholder] First case study headline that leads with the metric, then the mechanism.
“Placeholder quote — replace with a real client quote.”
[Placeholder] Second case study headline that leads with the metric, then the mechanism.
“Placeholder quote — replace with a real client quote.”
[Placeholder] Third case study headline that leads with the metric, then the mechanism.
“Placeholder quote — replace with a real client quote.”
Engineers who ship inside your systems,
not isolated AI demos
Talk to an FDE lead
Production from the first sprint
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Senior pods, not rotating benches
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Governance built in, not bolted on
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Measured against your metrics
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
We've been recognized by the best, year after year
- Inc. 5000
- Top 15 Inspiring Workplaces
- Titan Business Platinum (AI & Automation)
- Financial Times
- Mogul People Leader
- Forbes Coaches Council
- ISO 27001
- ISO 20000
- ISO 9001
- CMMI DEV 3
Tell us which system you want to modernize first
Clutch[Placeholder] Clutch badge and rating — add the official widget
“Placeholder testimonial — replace with the global set from tkxel.com.”
“Placeholder testimonial — replace with the global set from tkxel.com.”
Frequently asked questions
What are legacy system modernization services?
Legacy system modernization services update or replace aging software so it is easier to change, integrate and run, without losing the business rules it holds. Approaches range from wrapping a system with APIs, to replatforming or refactoring it, to replacing it entirely. tkxel’s pod favors incremental modernization: connect the system first, then replace the riskiest modules one at a time while the original keeps running.
What is the strangler fig pattern?
The strangler fig pattern replaces a legacy system gradually by routing one capability at a time to new code until the old system can be switched off. Martin Fowler named it after a vine that grows around a host tree. In practice, a facade or API gateway sits in front of the legacy system and sends each request to either the old or new implementation, so you can move and test one module at a time.
Should we rewrite, refactor or wrap our legacy system?
Wrap the system with APIs when its logic is sound and you mainly need access, refactor when the code is worth keeping but hard to change, and rewrite only a module whose logic is wrong or whose platform is unsupported. Most estates need a mix. We decide module by module during the assessment, scoring each by business value, change frequency and risk, rather than applying one approach to the whole system.
Which ERP and CRM systems do you integrate with?
We integrate with common ERP and CRM platforms, including SAP, Oracle, Microsoft Dynamics 365, NetSuite, Salesforce and HubSpot, as well as custom and mainframe-era systems. We use each platform’s supported APIs, integration services or certified connectors where they exist, and change data capture or file interfaces where they do not. If you already license an integration platform such as MuleSoft or Boomi, we build on it.
How long does legacy modernization take?
Incremental legacy modernization delivers its first working change in weeks and then continues module by module for as long as the estate needs. In our plan, the assessment and first API layer take about five to eight weeks, and each module replacement adds roughly one to two months. Replacing an entire ERP still takes considerable time; the difference is that you get usable results after every module instead of only at the end.
What are the risks of incremental modernization?
The main risk of incremental modernization is that running old and new systems in parallel adds temporary cost and complexity. You maintain the facade, sync data between both sides and reconcile results until each module cuts over. Programs also stall when the remaining legacy core is hard to split, so we state plainly in the assessment which parts may still need a larger replacement later, and what that would involve.
Can legacy modernization help us adopt AI?
Yes, modernization helps AI adoption because AI features need reliable access to the data and actions held in core systems. A documented API and event layer lets an assistant read order status, create a ticket or update a record through controlled endpoints with logging and permissions. Without that layer, each AI use case needs its own fragile extract, which is slow to build and hard to secure.