LEGACY SOFTWARE TRANSFORMATION SPECIALISTS

We solve legacy problems that previously, were too difficult, expensive or risky.

IOIntegrated solves hard, entrenched technology and operational problems with AI—especially when documentation is missing, expertise has moved on, a migration has stalled, or business teams carry real cost because of a system nobody wants to touch.

Discuss a stuck problem See how we work
Business-first Evidence-led Enterprise-ready
Legacy opportunity scan Diagnostic
01 Knowledge is disappearing
Investigate
02 Migration economics do not work
Reframe
03 Workarounds have become operations
Unlock
LEGACY CORE Stable, embedded, hard to change
AI
Investigation + transformation layer
NEW CAPABILITY Migration, access, automation, insight

Modernize the outcome—not automatically the entire core.

Where others see a replacement programme, we look for a faster path to business value.

WHERE WE CREATE LEVERAGE

Legacy is not about age.
It is about entrenchment.

A five-year-old platform can be legacy if it is deeply embedded, poorly understood, and uneconomic to change. We focus on the problems that sit between “keep patching” and “rip everything out”—and on the cost, friction and lost revenue they quietly generate.

01

Systems no one fully understands

Documentation is incomplete, experts have left, and critical behaviour lives in code, configuration, or institutional memory.

Recover knowledge and reduce dependency.
02

Migrations that do not make economic sense

Moving customers, data, rules, or configurations is possible in theory—but too manual, expensive, or risky in practice. Sometimes it has already been tried and has already failed.

Make the impossible migration viable.
03

Operations built around workarounds

Teams compensate for system limitations with paper, spreadsheets, manual checks, duplicate entry, and undocumented judgement.

Remove friction without destabilising the core.

WHAT WE DO

Three routes from an entrenched constraint to a real option.

Every engagement starts from the business problem, not from a service catalogue. Once we know what is actually stuck, one of these three routes usually carries the work.

02

Data Integration & AI Automation

Connect the systems you already have, remove the manual handoffs between them, and use AI where it enriches the loop—while execution stays deterministic and auditable.

For operations held together by re-keying, spreadsheets and undocumented judgement.

Explore the service
03

Odoo & Microsoft Dynamics ERP Deployments

Process re-engineering first, then an ERP rollout as an official Odoo partner—with AI-powered apps that keep the learning curve away from the people doing the work.

For growing companies still on paper, on outgrown tools, or facing a compliance deadline.

Explore the service

TWO WAYS IN

Not every engagement starts with a known problem.

The three routes above are where the work ends up. These are the two ways most clients get there.

Not sure where to start?

Ask for an AI Opportunity Audit.

A two-week, ROI-first diagnostic. We interview the people who do the work, map where time and money actually leak, test each candidate against what AI can reliably do, and hand back a ranked shortlist with the economics of each item and the smallest credible proof step.

For leadership teams that know AI matters and do not yet know which problem is worth it.

How the audit works

Want to level up both your software and your people?

Ask for AI Coaching.

Senior, hands-on coaching for the leaders and teams who have to work with AI day to day: from fluency, to building and demonstrating, to applying it in their own role. Designed so the people who own the system end up owning the solution.

For teams that will live with the outcome, not just sign off on it.

How coaching works

OUR APPROACH

Investigate first.
Transform what matters.

We combine senior business analysis, AI-assisted investigation, and hands-on implementation. The goal is not to add AI. The goal is to create a credible path from a stuck situation to measurable value.

DESIGN PRINCIPLE AI should improve the process without becoming an unnecessary point of failure.

Whenever possible, we use AI around the operational loop—learning, enriching, tuning, and improving—while keeping execution resilient, deterministic and auditable.

  1. 01

    Frame the business constraint

    We start with the blocked outcome: revenue, migration, service quality, cost, risk, or speed—not with a predetermined AI solution.

    Stakeholder interviewsWorkflow mappingValue hypothesis
  2. 02

    Reconstruct how the system really works

    Our EuclidAI investigation platform helps analyse sparse documentation, code, configurations, examples, logs, and observed behaviour to recover hidden knowledge—and proves each finding against the real system before it is trusted.

    System learningKnowledge recoveryConstraint discovery
  3. 03

    Qualify the path to value

    We compare impact, feasibility, data readiness, integration effort, risk, adoption, and operating cost before recommending a route—and we say so when a simpler approach is better than AI.

    ROI modelRisk assessmentOption ranking
  4. 04

    Prove and implement

    We build the smallest credible proof, validate it against real cases, and deliver running software with an implementation plan that fits the organisation’s governance and operating reality.

    Focused prototypeValidationEnterprise rollout

PRODUCTS & IP

Tools we built because the problems demanded them.

Neither of these started as a product. Each one exists because a client problem could not be solved with what was available—and the answer turned out to be reusable.

EUCLIDAI · SEMI-AUTOMATED AI INVESTIGATIVE PLATFORM

Built for cases with little documentation and few remaining experts.

Difficult legacy work usually starts as an information problem. EuclidAI reduces an unknown system, format or process to elements small enough to test directly, proves each interpretation against the real interface, and stores what it learns—so the next case starts further ahead than the last.

  • Learns from code, documents, examples, logs and observed outputs
  • Every interpretation is validated externally—the model never certifies itself
  • Proven knowledge is retrieved, not relearned, so each case accelerates
  • Surfaces undocumented rules, exceptions and dependencies

The platform supports expert judgement; it does not replace governance, validation, or accountable decision-making.

How EuclidAI works

TWO ANCHORS · AI-DRIVEN CORPORATE SDLC

Let business teams build with AI—without shadow IT, and without another translation chain.

A reference architecture and operating model in which AI generates only the user’s interaction with a system, never the system itself. Two stable artefacts—a canonical business view for people and a versioned intent catalogue for IT—anchor everything the AI produces in between.

  • IT keeps ownership of data, integrity, security and the headless application
  • Business owns the interface, its pace, and what it means
  • Generated surfaces can only speak the sanctioned intent vocabulary
  • Risk-tiered promotion: personal surfaces morph freely, shared ones are change-controlled, regulated ones are certified
Explore the Two-Anchor architecture
UX SKIN Generated, iterated, disposable. Holds only what the user sees and intends.
CANONICAL REPRESENTATION The fixed notation people comprehend and agree with. Stewarded by the business.
INTENT CATALOGUE Versioned, permissioned business intents—no raw CRUD. Engineered by IT.
CANONICAL DOMAIN LAYER Concepts, constraints and invariants, enforced before delegation.
SYSTEMS OF RECORD Engineered, tested, owned and operated by IT.
Business owns aboveIT owns below

PROOF POINTS · HARD PROBLEMS

Three constraints that stopped being constraints.

Named where we are allowed to name, anonymised where we are not. Each one began as a problem that had already resisted a conventional approach.

THE CONSTRAINT

Jitterbit’s legacy-platform customers were locked out of its modern cloud platform. Migration was a costly project that had already failed, and it had constrained growth for years.

Jitterbit THE UNLOCK

We built—then licensed to Jitterbit—an AI-based legacy project converter. The AI converts offline under constraint-based governance with an embedded audit trail. What ships is verified, deterministic migration tooling.

Hoursper migration, down from several weeks
Licensedthe converter now runs inside Jitterbit
DeterministicAI learns offline; the tooling runs verified

Followed by a three-part AI enablement programme for Jitterbit’s Professional Services team. Read more

Read the case Jitterbit Press Release

SGCLE · FRENCH GUIANA

From paper processes to AI agility.

OUTCOMECompliance deadline met on a modern ERP. The AI-powered apps required little or no user training.

A thriving multi-activity company ran on paper. New compliance requirements hit its electrical-equipment factory on an emergency timeline, with a workforce of limited computer literacy. Process re-engineering, an Odoo rollout and a voice interface kept the ERP’s learning curve away from the people who needed to use it.

Read the case

EV CHARGING · SITE SELECTION

Fifty candidate sites. Fifteen worth a visit.

OUTCOMETop-quartile-rated locations are measurably busier than bottom-quartile ones—validated against live occupancy data.

Choosing EV charging sites was expensive guesswork: shortlist dozens of locations, then evaluate them on the ground. A site-selection model built with AI reasoning applied offline to research and public data was validated against France’s QualiCharge open-data API.

Read the case Validation report

WHY CLIENTS BRING US IN

Senior judgement, practical delivery, and a bias toward value.

01

Business and technology in one conversation

We connect operating reality, financial impact, architecture, data, and adoption—without losing the thread between them.

02

Comfort in complex organisations

Experience across rail, energy, public-sector, fleet and industrial environments—large and regulated—informs how we handle governance and change.

03

Independent of a predetermined platform

We recommend AI only when it improves the business case—and advise against it when a simpler approach is better. We are not here to tell IT what it did wrong.

04

Designed for resilience

We favour architectures that remain controlled, inspectable, and operational even when AI is used to learn and improve around them.

START WITH THE PROBLEM

Bring us the issue that has been stuck for too long.

A first conversation is designed to determine whether the constraint is real, whether AI changes the economics, and what the smallest useful next step could be.

Confidential, senior-level discussion No assumption that a replacement programme is required A clear view of whether the problem is worth pursuing

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