Not a chatbot. Systems that do the work: pipelines that generate, judge and publish content on a schedule; coding agents that turn a Jira or GitHub issue into a reviewed pull request; workflows that watch, decide and act across your tools, and stop to ask a human before anything irreversible. Built on Claude, OpenAI and n8n, with guardrails and audit trails baked in.
Fixed-price pilot in two to four weeks · Runs in your cloud · Reply within one business day
Each one ships as a containerised service in your cloud, with tests around the decisions, a runbook, and monitoring that pages someone when it stops.
Generate, transform or classify at volume: marketing content, product descriptions, ticket triage, document extraction, lead enrichment. Every output passes an LLM-as-judge rubric before it goes anywhere, and rejected items are regenerated or escalated, never silently published.
Agents built on the Claude Agent SDK that pick up a Jira, GitHub, GitLab or Bitbucket issue, work in a checkout of your repo, open a pull request, respond to review comments, and run your deploy pipeline once a human approves the merge. Every tool call is streamed to a channel so the team sees what the agent is doing in real time.
Self-hosted n8n for scheduling, branching, retries and SaaS connectors, calling small typed services for the steps that need models, media processing or your database. Non-engineers can read the workflow; engineers can test the parts that matter.
Irreversible actions wait for a person. The automation posts a preview to Discord, Slack or Telegram with HMAC-signed approve, reject and edit links, plus a web dashboard for the backlog. One click, no login, no way to forge it.
Not everything needs a model. Event-driven rule engines with auto-execute versus approval-required policies, regex intent parsing, keyword classification with LLM escalation only for edge cases. Cheaper, faster, testable, and often more accurate than a prompt.
Image generation, text compositing, programmatic video with ffmpeg, and publishing to platform APIs including single posts, carousels, stories and reels. Credential lifecycle handled: long-lived tokens rotate on a schedule, not when they expire at 2am.
A demo that works once is easy. An automation that runs for months without embarrassing you needs the boring engineering: limits in code, approvals for anything irreversible, and a record of every decision. These are the rules every system we ship follows.
Our own products and internal tooling, described exactly as built. Client engagements are available as references on request.
We start with one workflow, not a platform. A pilot proves the value and the guardrails on real data; then we extend to the next process or hand it over.
Rates depend on scope. Ask, and you get a pilot price within a business day.
An agentic workflow is an automated process in which a language model does not just generate text but takes actions: it calls tools, reads and writes files, queries APIs, opens pull requests or posts content, then checks the result and decides the next step. The difference from a chatbot is that the output is a completed task, not an answer. The difference from a script is that the model handles the judgement calls a script cannot.
Both, usually. n8n is excellent for scheduling, branching, retries and connecting SaaS tools without writing glue code, and non-engineers can read the workflow. Anything involving model calls, judgement rubrics, media processing or your own database belongs in a small service that n8n calls over HTTP. We build that split: n8n for orchestration, a typed service for the parts that need tests.
Four layers. Scope: each agent runs in its own working directory and account with the minimum permissions for its job. Gates: irreversible actions such as publishing, merging, deleting or paying require a human approval, delivered as a signed one-click link in Discord, Slack or Telegram. Limits: daily caps, cooldowns and cost ceilings are enforced in code, not in the prompt. Visibility: every tool call the agent makes is streamed to a channel and written to an audit table, so you can see exactly what it did and why.
Less than most people expect when the pipeline uses the right model for each step. A strong model such as Claude Opus for the creative or reasoning step, a cheaper model such as Claude Sonnet for classification and moderation, and no model at all where a regex or a rule does the job. A content pipeline producing several posts a day across multiple pages typically runs for a few euros a day. We report per-run cost and set hard ceilings.
Anthropic Claude for generation, judgement and coding agents via the Claude Agent SDK; OpenAI for image generation and where a client already has a contract; open-weight models when data must stay on your infrastructure. We are not tied to a vendor and we design so the model behind a step can be swapped.
Yes. Everything we build is containerised and runs in your cloud account or on your servers. Model calls go to the provider you choose under your own API keys, and data that must not leave your network can be routed to a self-hosted model. You own the code, the workflows and the data.
Two to four weeks for one production-grade workflow, after a discovery workshop. Production-grade means it runs on a schedule, has guardrails and approvals, logs what it did, and someone gets paged when it fails. A demo takes an afternoon; a pilot is the thing you can leave running.
Describe the process, the systems it touches, and where a human has to stay in the loop. An engineer replies within one business day with a workshop slot and a pilot estimate.
NDA on request · Runs in your cloud · You own everything