AI automation agency.
Workflows that actually ship.

An AI automation agency designs and ships AI-powered workflows for your business — sales follow-up agents, support deflection, document processing, internal copilots. We're hands-on engineers, not slide-deck consultants. We map your highest-ROI workflows in a two-week audit, build a pilot in six to eight weeks, and run the systems with you in production. Every workflow has a measurable outcome and a reported cost-of-ownership.

Most production AI automation today is a combination of three things. (1) A foundation model — Claude, GPT-4, Gemini, or open-source like Llama — that handles natural-language reasoning. (2) Retrieval — pulling your private data (docs, tickets, CRM records) into the model's context via vector search, often with Pinecone, pgvector, or Weaviate. (3) Tools — letting the model call APIs in your stack to actually do things. We orchestrate these with frameworks like LangGraph and CrewAI, or write custom Python agents when the off-the-shelf options are over-engineered.

From kickoff to first workflow live: typically six to eight weeks after a two-week audit. The first two weeks are discovery and pilot scoping, the next four to six are building and integrating, and the final week is production hardening — logging, eval suite, runbook, fallbacks. Some narrow workflows (e.g. document classification with a clean dataset) ship in two weeks; complex multi-system agents take eight to twelve. We don't quote two-week timelines for work that takes two months.

They're complementary, not competitors. Traditional automation — Zapier, n8n, RPA — is best for deterministic, rule-based work: "when invoice arrives, save to drive." AI automation handles work that needs judgment: extracting data from unstructured documents, routing tickets based on intent, summarizing meeting transcripts, drafting replies in your tone. A well-built system uses both: the AI handles the messy reasoning, traditional automation handles the plumbing between systems.

We report cost-of-ownership monthly: dollars per workflow per month versus hours saved or revenue recovered. Typical patterns we've shipped: support deflection at $300–800 per month per workflow, saving 40+ hours of Tier-1 work. Invoice processing at $400 per month, saving 60+ hours. Abandoned-cart recovery at $200 per month with 15–20% revenue lift on the recovered cohort. ROI compounds because a shipped workflow keeps running while you build the next one.

Yes. Most automation work is integration work — the AI part is often 10% of the project. We integrate against Salesforce, HubSpot, NetSuite, Slack, Microsoft Teams, Jira, Zendesk, Intercom, Shopify, Stripe, and most modern SaaS via their APIs. For older systems without APIs, we build adapters: SFTP, email, browser automation as a last resort. We never ask you to migrate platforms to do automation.

All of them, picked per workflow. Claude wins on long-context reasoning and tool use; we use it for complex agents and document workflows. GPT-4 has the best ecosystem for structured outputs and function calling. Gemini is competitive on cost for high-volume classification. Open models (Llama, Mistral) run on your infra for cost-sensitive or compliance-sensitive workloads. The choice is a tradeoff per use case — latency, cost, quality, privacy — and we evaluate it openly.

Three engagement models. A workflow audit is $3,000 for two weeks of discovery, ROI scoring, and a 90-day roadmap. A pilot-to-production engagement for a single workflow is $10,000–$25,000 fixed price, six to eight weeks. An ongoing automation team is from $5,000 per month and includes a PM, AI engineer, and ops analyst shipping workflows on your roadmap. Cost-of-ownership for shipped workflows typically runs $200–$1,000 per workflow per month including model usage.

The right AI automation solutions depend on your highest-ROI workflows, not on what's trending. We see five categories pay back consistently. (1) Sales automation — lead qualification agents, outbound personalization, transcript-to-CRM, account research. (2) Operations and document workflows — invoice and PO processing, contract review, claim adjudication, exception triage. (3) Customer support automation — Tier-1 deflection with RAG, ticket routing, multilingual reply drafts. (4) Internal tools and copilots — meeting summarization, knowledge agents, Slack copilots, weekly digests. (5) Reporting and analytics — business digests, anomaly detection on KPIs, natural-language reporting. Most teams have a high-ROI candidate in three of these five categories. The audit ranks them so you don't have to guess.

Traditional business automation (RPA, no-code, scheduled scripts) handles deterministic work where the rules are known up-front: "when invoice arrives, save to drive, update spreadsheet." AI for business automation handles work that needs judgment: extracting data from unstructured documents, routing tickets based on intent, summarizing meeting transcripts, drafting replies in your tone, qualifying leads against a written ICP. A production-grade system uses both — AI for the messy reasoning, traditional automation for the plumbing between systems. We're vendor-neutral on this; if a workflow is better served by a Zapier-style flow, we'll say so at the audit stage instead of building you a custom agent.