Capability · 01
API integration
REST, GraphQL, SOAP. Authentication, authorization, rate limiting, retry, observability. API gateway selection and rollout where indicated.
RESTGraphQLSOAP
System Integration — API, Data Migration, and Legacy Modernization
API integration (REST and GraphQL), data migration, platform consolidation, ETL and data pipelines, legacy modernization, and AI-powered integration. We integrate first, modernize incrementally, replace only when there's no alternative.
API + middleware
Legacy migration
MQ/ESB
Replacement is the most expensive, riskiest path.
Integration is almost always cheaper, faster, and lower-risk — when it’s done right. Done wrong, integration becomes a tangle of custom adapters that nobody dares touch. We design integration architecture that survives the people who built it: documented patterns, observable pipelines, and a roadmap from “integrated” to “consolidated” so you have an exit when the legacy system finally retires.
What’s included.
Capability · 02
Data migration
Schema design, mapping, transformation, validation, cutover orchestration. Zero-downtime patterns where required.
Zero-downtimeValidation
Capability · 03
Platform consolidation
When your environment has accumulated three CRMs, two HRIS, four ticketing systems. We pick the survivors and integrate the rest, sunset the duplicates.
CRMHRISESB
Capability · 04
ETL and data pipelines
Airflow, dbt, Fivetran, Spark, Databricks. Cloud-native or self-managed.
AirflowdbtFivetran
Capability · 05
Legacy modernization
Strangler-fig replacement of legacy systems with the legacy live throughout. No big-bang go-live.
Strangler-figMQ
Capability · 06
AI-powered integration
Semantic mapping for legacy schemas, document-intelligence for legacy unstructured data, LLM-driven adapter generation.
LLMSemantic mapping
Map, architect, migrate, optimize.
PHASE 01 · 2–4 WEEKS
Map
Current-state integration topology, system inventory, data flows, technical debt assessment.
PHASE 02 · 3–6 WEEKS
Architect
Target-state design, integration patterns, observability plan, sequencing roadmap.
PHASE 03 · 8–32 WEEKS
Migrate
Phased implementation. Each system migrated, validated, cut over. Scope-dependent timeline.
PHASE 04 · ONGOING
Optimize
Pipeline monitoring, performance tuning, cost optimization, retirement of replaced systems.
Outcomes.
- An integration architecture your team can maintain — documented, observable, no mystery adapters.
- A roadmap from current state to target state with milestones, risk register, and exit criteria.
- Continuous-monitoring tooling on every pipeline so failures surface before they become data quality incidents.
Frequently asked questions.
What integration platforms do you work with?
MuleSoft, Boomi, Workato, Tray, n8n, custom code. Selection is part of the assessment if you're tool-shopping.
Do you do data engineering as well?
Yes — Airflow, dbt, Fivetran, custom Spark/Databricks. Integration and data engineering live in the same team.
What's your stance on iPaaS vs. custom?
Pragmatic. iPaaS pays off for repeatable patterns; custom pays off for high-throughput or highly-specific flows. Most environments need both.
Can you integrate legacy mainframe?
Yes — through MQ, CICS Web Services, REST wrappers, or schema-on-read patterns depending on the system.
What about real-time vs. batch?
Both. Real-time via event streaming (Kafka, Kinesis, Pub/Sub); batch via scheduled pipelines. Selection per data flow, not as a global choice.