Implement.
AI workflows, agents, and local deployments, with testing and handover.
Who it's for
- Ops teams
- Solo operators
- Sensitive-data orgs
Build AI systems your team can operate.
We build AI workflows, agents, and integrations around a defined task. Before implementation, we agree how the outputs will be tested, which actions need human review, and who will operate the system.
We work with your existing tools where they fit, and explain any additional infrastructure the system needs. Delivery includes the documentation, tests, and handover agreed in the scope, with hosting and ongoing support responsibilities made explicit.
Formats
- Production AI workflowsAutomations connected to your tools and data, with monitoring, error handling, and a defined operator workflow.
- Agent systemsMulti-step agents with tool access, validation, and human review points, tested against relevant tasks and data.
- Local AI deploymentsSystems on your own hardware, or a mix of local and cloud models, sized to the workload and its data requirements.
- Evaluation toolsRepeatable tests for output quality, failure cases, and model changes, with criteria agreed for the task.
Outcomes
- A system ready for the agreed useA deployment tested against agreed acceptance criteria, with clear limits and responsibilities for operation.
- Documentation and handoverSetup and operating instructions, plus training for the people responsible for the system.
- Tests and monitoringChecks for relevant failure cases and visibility into the system's behavior as it runs.
- Documented data boundariesAn explicit record of which data stays local, which services receive it, and the permissions involved.
How we work
- 01
Discovery
A free 30-minute call to discuss what you're working on and whether we can help.
- 02
Scope
A written proposal with deliverables, timeline, and price. You approve it before any work starts.
- 03
Deliver
We deliver the agreed work and review progress with you at each milestone.
- 04
Handover
We provide the agreed materials and handover. Any ongoing support is scoped with you.
Selected work
-
A growth team's ad audit agent.
Specialized AI agents audit an ad account and draft an optimization plan. A separate validator checks outputs against defined rules.
Checks at every hand-off -
A B2B team's lead research agent.
A system for company research, lead scoring, briefing decks, and outreach drafts. The team sets the qualification rubric and can inspect the evidence.
Hours to minutes per lead -
A campaign team's synthetic focus group.
AI-generated personas compare message variants and explain their responses. The simulated results support early review before testing with real customers.
Hundreds of personas per test -
A privacy-sensitive org's on‑prem AI.
An AI assistant and agent system on the client's hardware. A sensitivity-based policy controls which tasks run locally and which may use cloud models.
Sensitive workloads stay local
The stack we work with
Models, tools, and infrastructure used in AI work.
- OpenAI
- Anthropic
- xAI
- Meta
- Cohere
- Mistral AI
- DeepSeek
- Qwen
- Moonshot AI
- Z.ai
- TII
- Microsoft
- Perplexity
- Cursor
- Notion
- OpenClaw
- Cognition
- Manus
- Replit
- Lovable
- Midjourney
- Runway
- ByteDance
- Black Forest Labs
- ElevenLabs
- HeyGen
- Higgsfield
- Kling AI
- MiniMax
- Luma AI
- Suno
- n8n
- Zapier
- Make
- LangChain
- LlamaIndex
- CrewAI
- Pydantic AI
- NVIDIA
- AWS
- Hugging Face
- OpenRouter
- Groq
- Cerebras
- Together AI
- Replicate
- Vercel
- Ollama
- LM Studio
Get in touch
If you're not sure what to do about AI, start here.
Book a discovery call. Thirty minutes, no charge, to discuss what you're working on and whether we can help.
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