AI/ML
Autonomous AI agents that plan, reason, and execute multi-step workflows — built on proven frameworks and wired directly into your business systems.
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Years in production
What it is
AI agents are systems that use a language model as a reasoning engine to plan multi-step tasks, call external tools, manage memory across interactions, and take actions in software environments without continuous human instruction.
What you get
The difference between a chatbot and an agent is the ability to act. Agents use language models to break down complex goals, decide which tools to call, interpret results, and iterate — handling tasks that would otherwise require a human to orchestrate across multiple systems.
We build agents on LangGraph, CrewAI, and custom orchestration layers depending on the reliability, observability, and control requirements of the use case. Every production agent we ship includes deterministic guardrails, structured output validation, and comprehensive logging — because LLM-driven systems need auditable traces, not black boxes.
Common use cases we have shipped: competitive intelligence pipelines, automated code review agents, procurement research assistants, multi-channel support routing, and internal knowledge retrieval systems that work across PDFs, databases, and APIs simultaneously.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Deterministic guardrails and output validation
Human-in-the-loop approval workflows
Observability and agent trace logging
Autonomous task scheduling and retry logic
Integration with databases, browsers, and file systems
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
Built with
Tasks that require multiple steps, multiple data sources, and reasoning about what to do next — but where the logic is too variable to hard-code. Examples: research and summarisation across the web, automated data extraction from documents, code generation with test execution, and multi-system workflows like onboarding automation.
We build deterministic guardrails at every decision boundary — output schemas, confidence thresholds, sandboxed tool execution, and human-approval gates for high-risk actions. The goal is an agent that fails loudly and safely rather than silently and expensively.
A focused single-domain agent — one goal, defined toolset, known data sources — typically takes 4–8 weeks from discovery to production. Multi-agent systems with broad access to enterprise data and complex orchestration take 10–16 weeks.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
