Agentic AI Integration & Workflows

Build autonomous AI agents that act, decide, and integrate across your systems — designed for production from day one, not just demos.

6 wks

to production AI agent

10+

system integrations per agent

80%

reduction in manual workflow steps

99%

guardrail compliance rate

Overview

AI That Does the Work

Most AI integrations stop at the chatbot layer. We build agents that take real actions: reading your CRM, updating your database, calling your APIs, drafting communications, and escalating to humans only when genuinely needed. The architecture is designed around reliability, auditability, and graceful failure — because production AI is different from a notebook demo.

We choose the right model for each task rather than defaulting to the most expensive option. Structured outputs, function calling, and deterministic post-processing make AI behaviour predictable. Evaluation harnesses measure quality before deployment and flag regressions after.

Capabilities

What We Deliver

Agent Design & Architecture

  • Single agents for well-defined tasks with tool-use and function calling
  • Multi-agent orchestration with LangGraph, AutoGen, or CrewAI
  • State management: persistent memory, context windows, and checkpointing
  • Human-in-the-loop gates for high-stakes decisions
  • Agent observability: trace logging, step timing, cost tracking

LLM Integration

  • Anthropic Claude, OpenAI GPT-4o, Google Gemini, and open models (Llama, Mistral)
  • Prompt engineering, few-shot examples, and chain-of-thought design
  • Structured output with JSON schema validation
  • Model routing: cheap model for simple tasks, capable model for complex ones
  • Token budget management and context compression strategies

RAG & Knowledge Systems

  • Document ingestion pipelines: PDFs, Confluence, Notion, SharePoint
  • Embedding generation with OpenAI, Cohere, or open-source models
  • Vector store selection and configuration: Pinecone, Qdrant, Weaviate, pgvector
  • Hybrid search combining semantic and keyword retrieval
  • Retrieval quality evaluation and chunk strategy optimisation

Evaluation & Guardrails

  • Automated evaluation harnesses with golden test sets
  • Hallucination detection using reference-based metrics
  • Output validation with Pydantic and custom classifiers
  • Toxicity and policy compliance filters
  • Regression tracking in CI so quality doesn't decay over time
How We Work

Our Approach

01

Discover & Define

Map the workflow, define success criteria, and identify where AI adds genuine value vs where deterministic code is the right choice.

02

Prototype & Evaluate

Two-week prototype with an evaluation harness measuring quality on real examples before a line of production code is written.

03

Build for Production

Observability, error handling, retry logic, cost controls, and guardrails built in — not bolted on at the end.

04

Deploy & Monitor

Phased rollout with A/B comparison, usage dashboards, cost tracking, and alerting on quality regressions.

Technology

Stack & Tools

Python LangChain LangGraph AutoGen CrewAI Anthropic Claude OpenAI API Google Gemini Pinecone Qdrant Weaviate pgvector FastAPI Docker Kubernetes Pydantic
When to Engage

Is This Right for You?

You have repetitive knowledge-work processes taking hours every day

Document review, data extraction, report generation, email drafting — tasks that are too varied for simple automation but follow patterns an LLM can learn.

Your support team spends most of its time answering the same questions

A RAG-powered support agent backed by your documentation resolves common queries instantly, escalating edge cases to humans with full context.

You've tried a chatbot but it hallucinates or feels unreliable

The issue is usually architecture, not AI capability. Proper evaluation harnesses, retrieval design, and guardrails make the difference between a demo and a product.

You need AI integrated with real business systems, not just a chat UI

Agents that read from your CRM, update Jira tickets, query your database, and send Slack notifications — real tool use, not simulated actions.

Related Services

You May Also Need

Ready to build your first production AI agent?

Describe the workflow you want to automate and the systems it needs to interact with. We'll scope a prototype in one call.