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Enfav LLC

Core service lines

Services

How we put AI and modern software into production, and bounded ways to start.

  1. Agentic workflow engineering

    Multi-agent and tool-enabled orchestration that automates multi-step technical and business processes: task planning and DAGs, MCP and API actions, typed JSON contracts, retries, evaluations and human review, so decisions can be traced.

    A way to start — 2–4 week AI prototype: A narrow workflow demonstrated against measurable success criteria, on approved data, with handoff documentation.

    Deliverables

    • Workflow design, task planning and DAG orchestration
    • MCP and API tool integrations for agents
    • Typed JSON contracts, retries and failure handling
    • Evaluation harnesses, human review steps and audit trails

  2. Document intelligence and RAG

    Extraction and enrichment workflows, document classification and validation, and retrieval over your documents and records — embeddings, hybrid search, reranking and grounded answers — with retrieval quality measured.

    A way to start — Document and knowledge pipeline: Sources connected, governed ingestion and retrieval built, and checks and human review added.

    Deliverables

    • Document ingestion, extraction and enrichment pipelines
    • Classification and validation against your rules
    • Vector and hybrid search with reranking
    • Grounded answers with source references, and retrieval evaluation

  3. Systems and data integration

    Custom APIs and backends, workflow modernization, event processing, PostgreSQL and MongoDB pipelines, AWS and GCP services and internal web tools — typed, modular and observable.

    A way to start — Modernization component: An API, data synchronizer, service migration or case-processing module, delivered within an existing platform.

    Deliverables

    • APIs, backend services and data synchronizers
    • Event processing and PostgreSQL and MongoDB pipelines
    • Incremental modernization of legacy services and front ends
    • Internal web tools and dashboards

  4. AI-assisted QA and browser automation

    Playwright and MCP browser sessions, regression harnesses, CI/CD validations, structured evaluations, debugging and observability for release quality.

    A way to start — QA automation sprint: Critical paths automated, with regression evidence integrated into your existing CI/CD.

    Deliverables

    • End-to-end and regression test automation
    • Agentic browser sessions with Playwright and MCP
    • CI/CD validations and release checks
    • Debugging and observability with logs, metrics and traces

Bounded engagements

Ways to engage

  1. 2–4 week AI prototype

    A narrow workflow demonstrated against measurable success criteria, on approved data, with handoff documentation.

  2. Document and knowledge pipeline

    Sources connected, governed ingestion and retrieval built, and checks and human review added.

  3. Modernization component

    An API, data synchronizer, service migration or case-processing module, delivered within an existing platform.

  4. QA automation sprint

    Critical paths automated, with regression evidence integrated into your existing CI/CD.

Applied technology

AI and orchestration
LLMs, RAG, embeddings, reranking, MCP, workflow DAGs, evaluation harnesses
Software
TypeScript, Node.js, NestJS, Python, React, Next.js
Platform and quality
AWS, GCP, PostgreSQL, MongoDB, Redis, Kafka, Playwright, Jest, Datadog, CI/CD

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