AI workflows that handle the boring parts.

Mostly Claude (Anthropic) for reasoning, writing, and anything involving structured judgment — it's the most reliable model we've shipped to production for small-business workflows. OpenAI's GPT-4 / GPT-4o family when a specific capability fits better (image understanding, certain function-calling patterns). Smaller open models (Llama, Mistral) on the rare workflow where cost-per-call is the binding constraint and the task is simple enough not to need a frontier model. We pick per workflow, not per company — and we'll tell you why we picked what we picked.

You could, and you might even get a working demo. The difference is what happens when it's running on real production data. Most of the 3 months goes to: (1) edge-case discovery during shadow mode — real input data is messier than examples, (2) confidence-threshold calibration so the workflow knows when to ask a human, (3) integration hardening so transient failures don't drop work, (4) audit logging so when something goes wrong you can trace what happened. A demo handles the happy path; a production system handles the other 30% — and that's where the 3 months goes.

Good fits: repetitive work with clear inputs and outputs (drafting customer follow-up emails from a CRM record, generating monthly recap reports from data, reconciling invoices, classifying inbound leads, summarizing transcripts), or judgment work where the cost of being wrong is low enough that human review on a percentage of outputs is acceptable. Bad fits: anything with regulatory exposure where the AI being wrong creates legal liability (medical advice, legal opinions, financial recommendations to consumers), high-stakes one-off decisions, or tasks where the right answer requires context the model genuinely can't access. We'll tell you in the audit if your task is in the bad-fit category — that conversation is free, and we'd rather not build something that won't work.

We design every workflow with privacy in mind. Customer data only goes to a model under a signed enterprise data-processing agreement (we use API endpoints that don't train on inputs and have explicit no-retention policies — Anthropic and OpenAI both offer these). For workflows handling especially sensitive data (PII, payment, health, anything regulated) we deploy on Cloudflare Workers with strict egress policies — your data never leaves an audited boundary, and every input/output is logged for compliance review. PII redaction layers go in front of the model when the task doesn't actually require the identifying fields.

We design for graceful failure, not perfection. Every workflow has a confidence threshold — below it, the work routes to a human for review instead of going out the door. Errors are logged with full context (input, model response, why it routed for review) and surfaced in a daily summary so you don't discover a bad output two weeks later. For draft-and-approve workflows the human is always the final gate; for autonomous workflows we set the confidence bar high and accept that a percentage will get routed back for review — that's the cost of avoiding silent failures.

Yes — most of our workflows are designed as "draft + approve." AI generates the email, the report, the proposal; a human reviews and approves before send. The AI does the time-consuming part (writing the first draft, gathering the data, assembling the format); your team does the part that genuinely needs human judgment (final tone, business context, the call). Fully autonomous workflows exist for the cases where the cost of review outweighs the cost of an occasional wrong output, and we'll be honest about which mode fits each workflow.

Usually no — and we'll tell you up front when it might. Most automations remove the work people complain about (data entry, quarterly report assembly, the same email rewritten 80 times) so the team can spend time on the work that actually moves the business. When the data does point to genuine displacement (a process that's 100% automatable with no judgment layer), we'll lay out alternatives — redeployment, hour reductions, attrition — before recommending anything else. The goal is leverage, not headcount cuts.

Not autonomously — we don't fine-tune models on your data (the operational cost and risk usually doesn't justify the gain at small-business scale). What we do instead is quarterly tuning: we review the workflow's performance over the prior 90 days, identify where it's drifted or where the input shape has changed (new product lines, new customer types, new edge cases), and update the prompts, thresholds, and integration logic. Most workflows get meaningfully better at the 3-month, 6-month, and 12-month tune marks because the spec gets refined by real production data.

The monthly support plan ($200–$500/mo) covers small workflow changes — adjusting tone, swapping an input source, modifying an output format, raising or lowering a confidence threshold. Larger changes (adding a new branch, integrating a new system, building an adjacent workflow) get scoped as a separate small project. We're transparent about which bucket a change falls into before we touch it.

You own everything. Source code lives in a private repo you have access to from day one. We document the prompts, the integration spec, the model choice rationale, the confidence thresholds and why we set them where we did. If you ever want to take it in-house — your dev team picks it up, you switch to another agency, or you decide to run it yourself — you walk away with the code, the docs, the test cases, and a 1-hour walkthrough. No exit fee, no key dependency we hold.

Yes, but cautiously. Agents (AI systems that plan and execute multi-step tasks autonomously) are the right tool for some workflows — research summaries that need to pull from multiple sources, multi-stage data enrichment, certain customer-service flows. They're the wrong tool for most current small-business use cases because the failure modes are harder to bound and the cost-per-task is higher. When we do build agent-style workflows we lean heavily on human checkpoints at every meaningful decision and on tools the agent can call rather than open-ended action. We'll tell you in the audit whether your task is one where agents earn their complexity.

API usage is typically $20–$200/month per workflow at small-business volume, depending on how often it runs and how long the prompts are (we optimize for both during build). Plus the optional monthly support retainer ($200–$500) if you want us monitoring + tuning. Most clients save 20–40 hours/month of staff time in exchange for $250–$700/mo of API + support — the payback is well under a month for any workflow we'd recommend building.