One outcome: data your auditors trust and your models can learn from.

Cloud Migration.

Lift, refactor, or rebuild — chosen workload by workload, not by slogan. We design landing zones your security team will actually sign off on, sequence the migration around your business calendar, and build a cost model your CFO can defend in the next board meeting.

A production cloud estate that runs at 30–50% lower TCO than the on-prem baseline, with security and FinOps controls in place from day one.

Scope

• →Current-state assessment of workloads, dependencies, and data gravity
• →Target-state architecture per business domain
• →Wave plan, sequencing, and cutover playbooks
• →Landing zone, IAM, and security baseline

Deliverables

• →Workload portfolio with disposition (rehost / replatform / refactor)
• →Reference architecture and IaC scaffolding
• →Migration runbooks per wave
• →FinOps cost model with quarterly forecasts

How we work

• →Senior architect on every engagement, not a junior team
• →Two-week assessment, then waves of 6–12 weeks
• →Fixed-fee assessments, T&M execution against a sequenced backlog
• →Knowledge transfer baked into every wave

Data Migration.

Where most modernization programs die. We bring source-to-target reconciliation across ERP, CRM, EHR, and warehouse systems — schema mapping, validation harnesses, and a zero-loss cutover playbook that has survived more than 180 production migrations across the senior team.

Every reconciled record balances to the source system, every cutover is a non-event, and the new warehouse goes live without a side-channel of broken reports.

Scope

• →Source profiling, quality baseline, lineage discovery
• →Schema mapping with business owners in the room
• →Reconciliation framework — row, value, and aggregate
• →Cutover dry-runs and rollback playbook

Deliverables

• →Source-to-target mapping document
• →Validation harness with automated diff reports
• →ELT pipeline code, dbt models, orchestration DAGs
• →Cutover runbook + rollback procedure

How we work

• →Iterate against real data, not sample slices
• →Reconcile early, reconcile often
• →Cutover rehearsed at least twice in production-like environments
• →Business sign-off before we even mention "go live"

Legacy Modernization.

Mainframe, COBOL, AS/400, and on-prem .NET refactored into services your platform team can actually maintain. We favor strangler patterns over big-bang risk: incremental displacement, measured every quarter, with the original system kept hot until it isn't needed.

The legacy core is retired on schedule with no business interruption, and your team owns and operates the new platform without a vendor lifeline.

Scope

• →Application portfolio with retire / retain / rebuild disposition
• →Domain decomposition and service boundary design
• →Strangler architecture and routing layer
• →Decommissioning plan with measurable gates

Deliverables

• →Service catalog with API contracts
• →Reference implementation of the first two services
• →CI/CD pipeline and observability baseline
• →Decommissioning playbook

How we work

• →Pair with your team on every commit — not a black box
• →The legacy system stays in production until the gate is met
• →No big-bang cutovers; everything reversible until it isn't
• →Senior engineers on the keyboard, every time

Data Governance.

The practice that turns a data lake into an asset you can actually use. Catalog, lineage, classification, and PII protection — wired into the platform, not bolted on as a deck. Governance that holds up under audit and lets your AI program move faster, not slower.

Any analyst can trace a number on a dashboard to its source system in under five minutes — and your auditor can do the same.

Scope

• →Catalog rollout — domains, glossary, ownership model
• →Automated lineage from ingest to consumption
• →PII discovery, classification, masking at the boundary
• →Quality SLAs, observability, and audit trails

Deliverables

• →Catalog populated with the top business domains
• →Lineage graph wired to ingestion + transformation
• →PII inventory + masking policy enforcement
• →Data quality dashboard and alerting

How we work

• →Govern the high-value domains first — not boil the ocean
• →Treat governance as a product, not a policy PDF
• →Senior data stewards embedded with your team
• →Knowledge transfer until your team owns the tooling

Analytics & AI Enablement.

The last mile — where data becomes a decision, a forecast, or a model input. Semantic layers, governed self-service BI, and RAG-ready vector stores feeding LLMs that can be trusted because the data behind them can be. AI that earns the seat at the table.

An analytics & AI layer your executives use weekly — because the numbers are right, the latency is fast, and the answers cite their sources.

Scope

• →Semantic layer and metric definitions
• →Executive dashboards and self-service workspaces
• →RAG architecture and vector store wiring
• →LLM evaluation, guardrails, and PII-safe prompting

Deliverables

• →Governed semantic model with certified metrics
• →Dashboards and a self-service enablement program
• →Reference RAG implementation against your corpora
• →Eval harness for model quality and safety

How we work

• →Start with one decision worth getting right, not 50
• →Models cite sources or they don't ship
• →PII never leaves the perimeter — period
• →Hand off the production system to your platform team