Haink builds reliable enterprise integrations across ERP, CRM, IAM, messaging and observability systems — and embeds LLM and ML capabilities into the software you already run. A senior-only team standardizes interfaces, improves data quality and reduces operational overhead, with secure, observable pipelines.
Connect SAP, Salesforce, Dynamics and custom systems with reliable, well-documented interfaces and reconciliation.
REST, GraphQL and gRPC APIs with versioning, rate limiting, gateways and clear contracts (OpenAPI).
Kafka, queues and webhooks for resilient, decoupled, real-time data flows between systems.
SSO, OAuth/OIDC, SCIM provisioning and role-based access wired across your stack.
Idempotent synchronization and migrations with reconciliation, audit trails and zero-loss cutovers.
Embed LLM and ML features into your current applications and workflows without a rebuild.
Typical stack:
Production work delivered by our engineering team. Client names withheld under NDA; sectors shown to indicate context. See full case studies →
We embedded an automated drafting, SLA-monitoring and FAQ layer directly into the platform's existing case-management workflow — integration into live processes, not a standalone tool.
Identity verification wired directly into the lending pipeline, replacing slow manual review and reducing drop-off.
Yes. We embed LLM and ML features into your current applications, ERP/CRM and workflows through clean interfaces — no full rebuild required.
ERP (SAP, Dynamics), CRM (Salesforce and others), IAM/SSO providers, data platforms, observability and security tooling, plus custom in-house systems via REST/GraphQL/gRPC and messaging.
Idempotent operations, retries and reconciliation, contract testing, monitoring and audit trails — so data stays consistent across systems.
Yes — SSO, OAuth/OIDC, SCIM provisioning and role-based access control across the integrated stack.
Yes. We deploy connectors and pipelines within your network where data residency or security require it.
Let's shape a clear plan with milestones, architecture options and an implementation roadmap — with right-sized GPU hardware if AI workloads are involved.