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Services · AI, automation & agents

AI and automation built around real workflows, not demos.

Agents, copilots and automation that fit the way your team already works, with the evaluation and review steps that make them safe to rely on.

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Where the human stays in the loop
InputsDocumentsemail, CRM1AI agentLLM andyour tools2HumanReviews,approves3SystemsUpdated onapproval4changes requestedEvaluateGuardrailsMonitorCorrections become test cases. Alerts go to a named owner.
11case studies tagged Data, AI & Automation
We rethink sales processes with our own product, Sales Blackbox
2–6 wksa typical first engagement, ending in a clear go or no-go
Technologies behind this work
Models & agents
OpenAI
Claude
Google Gemini
Mistral
Hugging Face
Ollama
LangChain
CrewAI
MCP
PyTorch
TensorFlow
Automation & data
n8n
Supabase
Zapier
Make
PostgreSQL
Qdrant
Redis
Python
Node.js
Docker
GitHub
Retool
Temporal
What is included

Four areas, from first agent to reliable system.

We start with the workflow and the people who do it, then choose the model.

Agents & assistants

AI that does a step of the work.
  • AI agents & agentic systems
  • AI copilots & intelligent assistants
  • Generative AI & LLM applications, including retrieval over your own knowledge

Automation

Fewer repeated decisions and handoffs.
  • Business-process & intelligent automation
  • Internal tools around the workflow

Data & machine learning

Models and analytics where they pay off.
  • Machine learning development
  • Computer vision & natural language processing
  • Advanced & predictive analytics

Make it reliable

What separates a pilot from a product.
  • MLOps & LLMOps
  • AI evaluation, safety & governance
Typical situations

When teams come to us.

Workflow

A team repeats the same decisions every week

We map the workflow with the people who do it, find the steps worth automating, and build the smallest useful system.

Proof

You have an AI idea and no proof it works

We test the riskiest assumption first, in a short validation sprint, before any large build.

Trust

The demo impressed everyone, production scares them

We add the evaluation set, the review step and the exception handling that let people trust it.

What you get

Deliverables you will keep.

Concrete artifacts, documented and handed over.

A working AI workflow inside your tools
An evaluation set and success metrics
Human-review and exception handling
A named owner and an adoption plan
A runbook for the team
A pilot report with a clear go or no-go
How to start

Three ways to begin.

Larger work starts with a short paid discovery.

All offers
Build

AI Workflow / Internal Product Build

AI and internal tools around real workflows, not demos.

Custom scope
Engagement

AI / Data / Cloud Modernization Engagement

Modernize workflows, data, AI systems and cloud architecture.

Custom scope
Sprint

Validation Sprint

A short proof and decision checkpoint before you commit more time, money or roadmap.

Usually 2–3 weeks
Proof

Work you can open and check.

Prototypes and pilots, described as exactly that.

See all 11 case studies
Built
for paid use
AI service for property marketing
Durable processing, tested access and accounting boundaries, and a reviewed security audit.
4
pilot success criteria
Legal operations AI pilot
Accuracy, review time, adoption and exception handling, defined before the pilot.
Qualify
before human sales
AI sales qualification assistant
The shorter flow won, turning the product into a qualification layer, not a sales replacement.
Feasible
next gate: a live pilot
AI workflow automation prototype
Workflow feasibility proved and the segment clarified, with a live-team pilot as the next gate.
Where this ends

Not quite what you need?

Need the data platform underneath first?Cloud, infrastructure & dataWant to test the idea before building anything?Consulting & product designNeed testing and security for an AI system?Quality, security & compliance
FAQ

Questions about AI work.

What separates a demo from production?
A named owner, a real workflow, an evaluation set, a human-review step and a way to see failures. A demo needs none of them.
How do you measure value?
We agree the measures up front: accuracy against a reviewed sample, review time per item, adoption by the people who do the work, and how exceptions are handled. Our legal-operations pilot defined exactly those four.
What about data privacy?
We scope what data the model sees, where it is processed and who reviews outputs before anything sensitive is touched. The specifics follow your policies and are written into the plan.
How do you govern agents?
Scoped permissions, logged actions, human approval for irreversible steps, and evaluation before and after release. We wrote the longer version in our blog series on AI-agent governance.

Tell us about the workflow.

Describe who does the work today and what slows them down. We reply with a clear next step — usually within 24 hours.

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