AI integration services

AI capabilities for the SaaS you already run.

Infinity AI Lab helps B2B SaaS teams add AI to established products and workflows, without treating the whole platform as a rebuild. Start with a specific user task, the systems it touches, and how your team will judge whether it works.

Your existing stack, data requirements, and operational constraints guide the implementation. We define what needs to connect and how it will be evaluated before choosing models or frameworks.

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Product capability

AI Feature Integration

Add LLM-powered product features where users already work. Connect assistants, document processing, and semantic retrieval to your application data and existing APIs, rather than creating another disconnected interface.

Useful project scenarios

  • An in-product assistant that answers account-specific questions using permitted records and source references.
  • Document intake that extracts fields for review before they enter your existing approval flow.
  • Semantic search across customer knowledge or product records when keyword matching misses relevant context.

Engineering scope

  • Connect existing APIs and data sources; define tenant boundaries, access checks, and structured model responses.
  • Implement ingestion, retrieval, citations, and interface integration where needed, with handling for missing or unsupported answers.

Practical handover

  • Agree representative test cases and acceptance criteria for the feature during scoping.
  • Provide the agreed integration code, evaluation examples, and documentation for data refreshes, model settings, and known limitations.

Connected operations

AI Workflow Automation

Connect multi-step work across your SaaS and operational tools. Use tool-calling agents where interpretation is useful, deterministic steps where rules are clear, and human approvals before consequential actions.

Useful project scenarios

  • Support triage that gathers account context, drafts a response, and routes sensitive cases to a person.
  • CRM updates proposed from customer conversations, with validation and approval before records change.
  • Onboarding workflows that collect missing information and coordinate tasks across existing systems.

Engineering scope

  • Integrate CRM and support platforms through webhooks and API orchestration, with narrowly scoped tool permissions.
  • Model workflow state, approval checkpoints, timeouts, and duplicate-event handling so interrupted work has an explicit recovery path.

Practical handover

  • Document triggers, permitted actions, escalation rules, and the people responsible for approvals.
  • Hand over the agreed workflow code, configuration, and operating notes for reviewing failures and changing business rules.

Operational readiness

Production AI Engineering

Strengthen an existing AI feature or integration for real operating conditions. Address the API boundary, failure modes, evaluation coverage, and infrastructure your team needs to understand and operate it.

Useful project scenarios

  • An AI endpoint needs authentication, tenant isolation, and clearer limits before wider customer access.
  • A live feature has inconsistent answers, unexplained failures, or rising latency and model costs.
  • An internal prototype needs to fit your deployment process, cloud environment, and on-call responsibilities.

Engineering scope

  • Build or harden APIs, authentication, background jobs, bounded retries, and rate-limit handling around existing application contracts.
  • Add observability and evaluations for representative tasks, with attention to sensitive data in logs and traces.
  • Profile performance and cost; assess caching, model selection, and cloud deployment against agreed workload assumptions.

Practical handover

  • Document deployment, rollback, monitoring, and incident investigation for the agreed components.
  • Provide evaluation baselines and known trade-offs so your team can assess later model or prompt changes.

Scope the integration before choosing the stack.

Bring a product workflow, an existing AI feature, or an integration problem. We’ll clarify the system boundaries, data access, acceptance criteria, and handover, then propose a scope and commercial terms that fit the work.

Discuss Your Project