The Core Squad

AI · AI Agents

AI agents that do the work, not just the demo.

Most companies experimenting with AI end up with a demo that never reaches production. This is our engineering practice for closing that gap. We design, build, and operate AI agents and LLM-powered applications that hold up under real traffic, real edge cases, and real security requirements.

Built with

OpenAILangChainn8n

Every project includes

  • Weekly demos in staging
  • Documented rollback plan
  • Docs and handover
  • Support after launch

How we approach it

We treat an AI agent as a piece of production software, not a prompt. That means proper architecture: a retrieval layer that grounds responses in your actual data, guardrails that define what the agent can and can't do, structured escalation paths for anything outside its confidence threshold, and full audit logging so you can see exactly what it did and why.

Our AI engineers work across the stack, from the LLM orchestration layer down to the vector database and API integrations that connect an agent to your CRM, support desk, or internal tools. We've shipped agents that resolve customer support tickets, internal copilots that search across scattered company knowledge, and document-intelligence systems that extract structured data from unstructured text at scale.

See it working before you talk to anyone.

What we build

AI Agents, end to end.

01

Custom AI agents & copilots

Purpose-built agents for support, sales, internal operations, or research, designed around your workflows, not a generic template.

02

LLM-powered applications

Full applications with an LLM at the core: content generation tools, research assistants, decision-support systems.

03

Chatbots for support & sales

Conversational interfaces that resolve real queries against your live data, with clear escalation to a human when needed.

04

NLP systems & document intelligence

Extraction, classification, and summarization pipelines for contracts, support tickets, records, and unstructured text at volume.

05

RAG pipelines & knowledge retrieval

Retrieval-augmented generation built on your own knowledge base, with chunking, embedding, and ranking tuned for accuracy.

06

AI app testing & evaluation

Evaluation harnesses and regression testing so model or prompt changes don't silently degrade accuracy in production.

How it runs

From first call to production.

  1. 1

    Discovery call

    The problem, the people affected and what success looks like, in your words.

  2. 2

    Written plan

    Scope, success metrics and what you will see each week, agreed before any code.

  3. 3

    Weekly builds

    Working software in staging every week, not a reveal at the end.

  4. 4

    Staged launch

    Released in steps, with monitoring and a documented way back.

  5. 5

    Handover or support

    Docs, access and training, or we stay on to run and improve it.

FAQ

Questions Answered.

Ask us anything

A focused first agent (one workflow, one integration) typically takes 3 to 6 weeks from scoping to production. Multi-workflow agents with several integrations run 8 to 12 weeks. We scope this precisely in the first working session.

We're provider-agnostic and select based on your requirements: cost, latency, data residency, and capability. We've built on OpenAI, Anthropic, and open-weight models, often with a fallback or routing layer so you're not locked to one vendor.

Retrieval grounding against your verified data, confidence thresholds with human escalation, and an evaluation suite we run before every deployment. We design the failure mode to be 'ask for help,' not 'guess.'

Yes, this is most of the engineering work in practice. We've integrated agents with common CRMs, help desks, and internal databases via API, and can build custom connectors where no off-the-shelf integration exists.