The agent lives in your repo. Cancel us and it keeps running.

Zapier is fine for two-tool, low-stakes glue. The moment your workflow needs branching, retries, audit logs, or an LLM call inside a step, it gets fragile fast. We build the layer underneath: real code, with tracing you can read, and we only reach for a model when classification or generation is the actual job. The result lives in your repo, so your team can take it over whenever you want.

Zapier is great for two-tool, low-stakes glue. The moment a workflow needs branching logic, retry handling, audit logs, or LLM calls inside a step, you hit the wall fast. We build the next layer down — custom code with proper observability — and only reach for an LLM when classification or generation is the actual job.

Whichever fits the task, and we tell you which one and why in the audit. The choice stays swappable behind an abstraction layer, so you are never locked to one provider. Your API keys stay in your environment. We never hold them.

Whichever fits the task. Claude for reasoning and code-adjacent work, GPT-4 for general agents, Gemini for cost-sensitive classification, locally hosted Llama for privacy-sensitive enterprise jobs. We tell you which model and why in the scoping doc, and we keep the choice swappable.

They keep working. The code is in your repo, on your infra, with the runbook and dashboards. A monthly retainer is optional. It covers alerts, edge-case fixes, and model swaps as providers update. You are month to month, so you can keep it for a sprint or two and then move ops in-house. No lock-in.

They work. Every build ships with a runbook, monitoring dashboards, and an on-call escalation path. A monthly operating retainer is optional — it covers alerts, edge-case fixes, and model swaps as the providers update. Many teams take it for the first six months and then move ops in-house.

In your infrastructure: your Postgres, your Supabase, your VPC, your bucket. We never run a shared, multi-tenant database with your data in it. The mutual NDA is signed before we look at anything, vendor contracts are confirmed not to train on your inputs, and everything we build is yours to take on day one.

In your infrastructure. Postgres, Supabase, your VPC, your S3 bucket. We never run a multi-tenant database. API keys for the models stay in your environment variables. Everything we build is fully transferable on day one.

Both. Internal LLM tools and custom CRMs are a regular build: admin pages, role-based access, audit logs, with the automations layered on top. Built around how your team actually works, not bent out of a tool you have to adapt to. Two QA passes before anything ships.

Yes. Custom CRMs are a regular engagement — same stack as the automations, with admin pages, role-based access, audit logs, and the workflow automations layered on top. Designed around your team's actual workflow, not adapted from Salesforce.

Three ways, wired before any live traffic. Retrieval grounds answers in your own knowledge base instead of guessing. A confidence threshold sends low-confidence answers to a person, not to your customer. And an eval suite runs on every prompt change to catch quality slipping before it ships. Where the agent claims a fact, it shows the source.

Every flow has a retry policy, a fallback path, and a Slack alert when both fail. The run log records every invocation with its status and duration. Edge cases that surface in production get added to the test suite. Nothing fails silently.