Services

AI Backend, Integration & Modernization Engineering

The backend, integration, retrieval, approval, and modernization layers that make LLM-driven workflows reliable in production: production RAG, MCP tool gateways, deterministic PLAN → EXECUTE → COMPOSE orchestration, LLM evaluation harnesses, policy-controlled tool gateways, and legacy shadow-core modernization.

What we build

One execution layer, many connected capabilities

From retrieval over messy documents to computer vision intake, AI-assisted commerce, technical lab signal workflows, and the backend underneath — each capability is part of the same production-grade system, not a separate service line.

Enterprise RAG & Hybrid Search

Retrieval systems that work on messy enterprise data: PDFs, contracts, tenders, policies, emails, reports, tables, and internal knowledge bases. Vector search, keyword search, reranking, metadata filtering, and source citations combine to produce grounded answers.

  • Hybrid search — BM25 + vector search with Reciprocal Rank Fusion
  • Cross-encoder reranking and metadata filtering
  • Source-grounded answers with inline citations
  • Multi-source indexing and permission-aware retrieval

Document Intelligence & Structured Extraction

Pipelines that parse complex documents and convert them into reliable structured data: PDF tables, scanned documents, legal judgments, procurement notices, invoices, forms, and operational reports.

  • PDF parsing, OCR workflows, and table extraction
  • Markdown conversion and JSON extraction with schemas
  • Validation rules and confidence scoring
  • Human review loops for high-value or uncertain cases

Deterministic AI Workflow Orchestration

Controlled AI workflows instead of uncontrolled autonomous agents. Each workflow follows defined states, business rules, approval steps, and audit logs.

  • LangGraph-style state machines and conditional routing
  • Tool execution with structured contracts
  • Workflow state persistence and webhook-based approvals
  • Human-in-the-loop approvals and escalation paths

Production Voice AI & Call Automation

Sub-300ms latency conversational voice systems wired securely into enterprise backend systems with strict human-in-the-loop escalation.

  • Low-Latency Native Audio Streams: Direct integration with native speech-to-speech models and optimized WebSockets/streaming infrastructure (Vapi, LiveKit, ElevenLabs) to match natural human conversation pacing.
  • Context-Aware Voice RAG: Specialized, ultra-fast vector search caching and semantic routing to fetch enterprise knowledge, compliance boundaries, and user records in milliseconds mid-call.
  • Deterministic Voice Guardrails & Escapes: Real-time validation layers that monitor audio outputs, prevent hallucinated commitments, and execute seamless state handoffs (transferring live audio + full transcripts) to human operators when edge cases or anomalies are detected.
  • Full System Tool Execution: Safe function-calling architectures enabling voice agents to reliably trigger webhooks, query databases, and update CRMs/ERPs mid-conversation without breaking system state.

AI Backend Engineering

The backend infrastructure required to run AI systems in production: APIs, databases, queues, storage, authentication, monitoring, cost controls, and integrations.

  • FastAPI services, PostgreSQL, Qdrant / pgvector, Redis
  • Background jobs, queues, and async workers
  • Docker-based deployment with secure configuration
  • Observability, cost controls, and API integrations

Evaluation, Guardrails & Auditability

Measure and control AI behavior before deploying it into real business operations. Evaluation pipelines, regression tests, guardrails, audit logs, and quality dashboards.

  • RAG evaluation — faithfulness, context recall, answer relevance
  • Hallucination checks and prompt injection controls
  • Sensitive data protection and PII handling
  • Audit trails and operator dashboards

Legacy System AI Modernization

Add AI capabilities around existing systems without risky big-bang replacement. API layers, shadow systems, comparison engines, and workflow overlays for gradual modernization.

  • Strangler pattern with modern API layers
  • Shadow architecture and comparison engines
  • Read-only adapters and data synchronization
  • Phased migration support with audit logs

Computer Vision Workflow Systems

We build practical computer vision workflows where images become structured operational inputs — for example, guided measurement, visual recommendation, review queues, and quotation workflows.

  • Photo intake with calibration references (e.g. A4)
  • Homography, guided corner selection, and admin review
  • Visual previews and image-based recommendations
  • Review queues that combine CV with operator confirmation

AI-Assisted Commerce & Personalization

We help commerce workflows use AI for personalization, guided product selection, customer intake, content generation, and operational automation.

  • Photo and preference-based customer intake
  • Personalized product guidance and shortlists
  • Checkout, logistics, and IOSS/VAT handling
  • Customer support and operational automation workflows

Technical Lab & AI-Ready Signal Workflows

We support technical lab concepts where RF, SDR, signal data, dashboards, and AI-ready analysis workflows can be combined for training, research, and operator support.

  • RF/SDR training infrastructure and hands-on workflows
  • Spectrum capture, labelling, and event datasets
  • AI-assisted classification and anomaly detection
  • Operator-assistance dashboards over lab signals

Custom Integrations

We connect AI systems and backends to the tools real teams use — CRMs, ERPs, email, WhatsApp, partner portals, internal databases — through clean, documented contracts.

  • CRM and ERP integrations with typed contracts
  • Email, WhatsApp, and partner portal connectors
  • Database adapters and event-driven workflows
  • Authentication, retries, audit logs, and observability

Technical capabilities

Frameworks are tools, not the product

We use LangChain, LangGraph, LlamaIndex, MCP, OpenAPI tools, and custom orchestration patterns where they fit — but we do not force framework lock-in. For production systems, we prioritize clear APIs, auditability, observability, retries, permissions, cost control, and maintainable execution flows.

  • LangChain / LangGraph agent workflows
  • LlamaIndex / custom RAG pipelines
  • MCP and OpenAPI tool interfaces
  • Vector databases and hybrid search
  • Human-in-the-loop approval flows
  • Async workers, queues, retries, and audit logs
  • Cloud, hybrid, and on-prem deployment
  • Observability, evaluation, and cost monitoring

How we engage

A bounded first step, then a production system

Most engagements start small and grow with evidence — a proof of confidence on real documents, then a production system, then expansion into additional workflows.

01

Individual Engineer Placement

For clients who need one strong backend or integration engineer embedded into an existing team.

02

Dedicated Nearshore Team

For clients who need a small delivery pod combining backend, data, DevOps, and AI integration skills.

03

Project-Based Delivery

For defined integration or AI backend implementation projects with a clear scope and outcome.

Start with a productized first engagement

Enterprise MCP / OpenAPI Gateway Sprint

2–4 weeks · fixed scope

Our fixed-scope AI integration sprint

Expose one internal system safely to AI agents. We map a selected workflow or legacy system, design secure AI-callable tools, implement an MCP / OpenAPI / FastAPI gateway, and add authentication, permissions, validation, logging, and audit trails so AI can interact with real business systems under control.

  • Map one internal system — database, portal, ERP, CRM, or workflow
  • Design safe AI-callable tools with explicit input/output contracts
  • Implement an MCP / OpenAPI / FastAPI gateway
  • Authentication and tool-level permission checks
  • Structured logging and audit trail for every tool call
  • Error handling, validation, and deterministic safeguards
  • Documented code and deployment notes you keep
  • Optional demo workflow connected to an LLM or agent interface

Controlled AI Workflow Sprint

Take one process from prototype to controlled execution

Available as a fixed-scope Controlled AI Workflow Sprint: one operational workflow, one controlled execution path, one operator interface, measurable acceptance criteria, and documented deployment architecture.

Next step

Bring AI into your documents, data, and workflows

Tell us about your documents, images, technical signals, data sources, or operational workflow. We will respond with an architecture path and a realistic plan from proof of confidence to production.