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. Integrate first, modernize incrementally.

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Connect everything. Replace nothing you don’t have to.

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

API integration — REST, GraphQL, SOAP. Authentication, authorization, rate limiting, retry, observability. API gateway selection and rollout where indicated.

Data migration — schema design, mapping, transformation, validation, cutover orchestration. Zero-downtime patterns where required.

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.

ETL and data pipelines — Airflow, dbt, Fivetran, Spark, Databricks. Cloud-native or self-managed.

Legacy modernization — strangler-fig replacement of legacy systems with the legacy live throughout. No big-bang go-live.

AI-powered integration — semantic mapping for legacy schemas, document-intelligence for legacy unstructured data, LLM-driven adapter generation.

DELIVERY MODEL

Map, architect, migrate, optimize.

  1. Map (2–4 weeks)

    Current-state integration topology, system inventory, data flows, technical debt assessment.

  2. Architect (3–6 weeks)

    Target-state design, integration patterns, observability plan, sequencing roadmap.

  3. Migrate (8–32 weeks)

    Phased implementation. Each system migrated, validated, cut over. Scope-dependent timeline.

  4. Optimize (ongoing)

    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.

Ready to talk — or still evaluating?

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Tell us what you're working on. Security, modernization, staffing — whatever it is, you'll hear back from a senior person within 48 hours. Not a sales rep. Not a chatbot.

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