The hardest technology decisions are made before anyone writes code. We settle architecture, sequence and delivery, whether you are building new or fixing what you inherited.
IT consulting and strategy services answer the question engineering alone cannot. Given what the business has committed to, what should be built, bought, changed or left alone, and in what order. That question is the same whether you are looking at an estate assembled over a decade or a product that does not exist yet. In both cases the decisions made earliest are the ones that cost the most to reverse.
We work from evidence, not from a framework applied to your business from the outside. For systems already running, that means examining applications, integrations, data, cloud footprint, delivery practice and security posture. For something new, it means testing feasibility and sizing effort honestly. What you receive is a target architecture and a sequenced roadmap. Not a slide deck.
Which initiatives run first, what each depends on, and which cannot start at all until an architectural or data problem is dealt with. Most roadmaps fail here, not on ambition.
For every application that matters, whether it is retained, refactored, re-platformed, replaced or retired, and the evidence behind that call.
Which work belongs in-house, which does not, and what the delivery team has to look like to sustain the roadmap after the consultants leave.
Lead time and change failure rate are climbing while the team has not shrunk. This is almost never a productivity problem. It is coupling, fragile environments or test coverage that stopped being trusted.
Reporting disagrees between systems and nobody can say which number is right. Integration grew one connection at a time, and no system was ever made the owner of anything.
Workloads were lifted rather than re-architected. Cost now scales with infrastructure instead of with customers, and each new client makes the margin worse.
Build versus buy, monolith versus services, or a vendor renewal is pending. The debate has run for months because nobody owns the analysis that would end it.
The demo worked. Then data quality, access control, evaluation and running cost turned out never to have been designed for production. This is the most common AI failure we see, and it is rarely about the model.
The business case is approved. Architecture, platform choice, integration model and team shape are not. Every one of those gets harder to change the moment code exists.
A commercial product might cover most of the requirement. Nobody has compared total cost of ownership, extensibility and exit risk against building it, so the decision keeps getting deferred.
Assessments are led by solution architects who have shipped and run comparable systems. People who have had to live with an architecture decision give different advice from people who have only recommended one.
Tie the technology agenda to what the business has committed to, define the target operating model, and sequence initiatives with dependencies, effort and risk against every move.
Test feasibility and platform options before budget is committed, or document current and target architecture where systems already run. Service boundaries, integration contracts and data ownership are settled here.
Score every application on business value, technical condition, cost and risk, then commit to a disposition. Run to a transaction timetable, the same analysis becomes technology due diligence.
Choose the migration approach per workload, design the landing zone, and model cost before and after. On the data side, quality and lineage come first, because AI programs stall there far more often than on models.
Measure delivery performance, then name the few changes that will move it. Controls are mapped against the obligations that apply to you, with remediation ordered by exposure rather than by ease.
We use established frameworks where they earn their place, and scale them to the size of the estate. Applied ceremonially, most of them produce documentation nobody reads.
C4 model views for context, containers and components, supported by architecture decision records so reasoning survives team changes.
Portfolio scoring against business value and technical condition, mapped to retain, refactor, re-platform, replace or retire, with a documented rationale per application.
AWS and Google Cloud well-architected reviews across reliability, security, performance, cost and operational excellence, plus FinOps practices for tagging, allocation and unit cost.
Deployment frequency, lead time for change, change failure rate and mean time to restore, measured before and after so improvement is evidenced rather than asserted.
Control mapping against ISO 27001 and your applicable regulatory obligations, threat modeling on the highest-exposure flows, and data lineage, ownership and quality profiling.
| Engagement | Typical scope | Key deliverables | Timeline |
|---|---|---|---|
| Solution Design and Feasibility | Feasibility, platform and build-versus-buy comparison, initial architecture and data model, and delivery shape for a new product | Technical feasibility assessment; build-versus-buy and TCO analysis; conceptual architecture and logical data model; NFR catalog; delivery estimate and team topology | 2 to 4 weeks |
| Technical Assessment Sprint | Focused review of one system, integration landscape or delivery practice | Current-state assessment report; architecture decision records; RAID log; prioritized remediation backlog | 2 to 3 weeks |
| IT Strategy and Architecture | Full current-state assessment, target architecture, portfolio disposition, integration and data plan, and sequenced roadmap | Current-state baseline in C4 views; target architecture and ADRs; portfolio disposition matrix; integration contract catalog and data lineage map; cloud landing zone design; TCO model and business case; sequenced roadmap with RAID log; target operating model and RACI | 4 to 8 weeks |
| Technology Due Diligence | Independent review of architecture, code quality, scalability, security, technical debt and team capability for a transaction | Due diligence report; code quality and technical debt assessment; scalability and NFR gap analysis; security posture and control gap analysis; post-close remediation plan and cost envelope | 1 to 3 weeks |
| Advisory Retainer | Ongoing architecture review, decision support, delivery governance and roadmap adjustment alongside your internal team | Architecture decision records; roadmap revisions and re-sequencing; design review notes per cycle; DORA metrics review | Monthly, ongoing |
Cost depends on the requirement: the number of systems in scope, how much documentation survives, the access and stakeholder time available, whether a transaction timetable applies, and whether the engagement covers an existing estate, a new initiative or both. Innogent Technologies confirms an estimate after scoping, and agrees the systems in scope, the access required, the documents you will receive and the checkpoints before work begins.
Estates with little surviving documentation take longer to assess because behavior has to be established from code, telemetry and the people who run the systems rather than read from a source. We say so before starting rather than discovering it in week three.
For an existing estate, stakeholder interviews and review of applications, integrations, code, infrastructure, telemetry, delivery metrics, security posture and cost. For a new initiative, the requirement, constraints, data sources, users and the technical questions that must be answered before commitment
1 to 3 weeks
Buy the advisory work alone if that is what you need. There is no obligation to use us for implementation, and the report is yours either way.
Where implementation follows, the architects who documented the target state stay involved, and custom software, modernization, cloud, data and AI engineering teams execute the roadmap at the pace the business can absorb.
Where you plan to build in-house, engagements can include documentation handover, architecture coaching and delivery process setup for your own engineers.
Answers about consulting scope, deliverables, timelines, architecture decisions, platform recommendations, AI strategy and roadmap execution.
IT consulting and strategy services assess an organization's applications, architecture, data, cloud footprint, delivery practices and security posture, then define a target state and a sequenced roadmap to reach it. The output from Innogent Technologies is documentation that supports investment and execution decisions rather than production code.
Software development builds an agreed solution. IT consulting establishes what should be built, bought, migrated, refactored or retired in the first place, and in what order. Consulting engagements produce assessments, architecture and roadmaps rather than working software.
Depending on scope: a current-state assessment, target-state architecture with decision records, application portfolio disposition, integration blueprint, cloud and data plans, delivery operating model, risk register and a sequenced roadmap with a business case. The document set is agreed before the engagement begins.
A focused assessment sprint takes 2 to 3 weeks, a full strategy and architecture engagement 4 to 8 weeks, and technology due diligence 1 to 3 weeks. Cost depends on the requirement: the number of systems in scope, how much documentation survives, and the access available. Innogent Technologies confirms an estimate after scoping.
Yes, and it is often the better time to engage. A solution design and feasibility engagement establishes whether the idea is technically viable, compares platform and build-versus-buy options, defines the initial architecture and data model, and sizes the delivery effort before budget is committed.
Each application is scored on business value, technical condition, operating cost, risk and dependency, then assigned a disposition with a documented rationale. Applications carrying high business value and poor technical condition usually take priority, because they combine the greatest exposure with the clearest return.
Recommendations are made on fit, total cost of ownership and exit risk rather than partnership. Where a commercial platform is the better answer than building, the assessment says so, including the licensing and lock-in implications.
AI use cases are scored against data availability, quality, risk and expected value before any model is selected. Each prioritized case is defined with its data sources, access controls, evaluation method, human oversight points and expected operating cost, so the pilot has a route to production rather than stalling after the demo.
Yes, and the engagement does not require it. Consulting can be bought as independent advisory work and the documentation is yours regardless. Where implementation follows, the architects who defined the target state stay involved.
Success measures are agreed at the start and typically include delivery metrics such as lead time and change failure rate, cost measures such as cloud unit cost and license spend, and risk measures such as closed remediation items. Baselines are captured during assessment so change can be evidenced.
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