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AI & Automation

AI that ships as a feature, not a science project

We run our own company on a multi-agent system we built on Amazon Bedrock. Four agents, operating unattended since August 2026, doing the publishing, research and engineering work this business depends on. The agent deployment below is that same build, pointed at your problem instead of ours.

See what it actually runs

Why take our word for it

Most people selling AI agents have a demo. We have a production system running our own business, and you can inspect the output: four agents on Bedrock, AWS managed in Terraform, continuous deployment, and over five hundred commits of agent-produced work in the first seven weeks.

It also taught us where agents stop being useful, which is the part worth paying for. They handle bounded, repeatable work with a clear definition of done. Hand one a vague goal and walk away and you get confident nonsense. We know where that line is because we crossed it a few times.

The full detail is on the work page

Done-For-You Autonomous AI Agent Deployment

$7,500 – $25,000+

A self-managing, multi-agent AI workforce deployed directly into your own AWS account. Zero-key IAM, Terraform IaC, GitHub Actions CI/CD, PM2 process management.

  • Terraform IaC package: EC2 provisioning, zero-key Bedrock IAM roles, scoped security groups
  • GitHub Actions CI/CD: automated lint, validation, zero-downtime deploy
  • Tiered scope, from a single agent daemon to a multi-agent suite with custom CRM/Slack/DB integrations

What that price covers Three tiers. $7,500 deploys a single agent daemon into one AWS account. $15,000 covers a three-agent suite with the full CI/CD pipeline. $25,000+ adds custom tool integrations and multi-region failover.

What changes it

  • Number of agents and how many distinct jobs each one owns
  • Custom tool integrations: CRM, Slack, ticketing, or a database the agents write to
  • Multi-region failover and an uptime commitment, versus single-region best-effort
  • Ongoing prompt tuning and log auditing, quoted as a monthly retainer on top

AI Readiness & Data Audit

$4,500

Before you commit to an AI build: a diagnostic engagement that tells you what's actually feasible, what it costs to get there, and what to build first. A scoped report and roadmap, not a sales pitch for a bigger engagement.

  • Data readiness assessment: quality, structure, and access gaps that would block any AI project
  • Security and compliance review scoped to AI use (PII exposure, model access boundaries, data residency)
  • A ranked shortlist of AI use cases specific to your business, each scored on effort vs. payoff
  • Written roadmap and budget range for the top use case, yours to act on with us or anyone else

What that price covers One week, one business unit, up to three data sources we're given read access to at kickoff.

What changes it

  • Additional business units or departments, each assessed separately
  • More than three source systems, or systems with no documented schema
  • Regulated data: HIPAA, PCI, or a residency requirement that needs its own compliance review

Internal Knowledge Assistant (RAG) Setup

$12,000 – $15,000

A chatbot that actually knows your business: answers pulled from your own docs, wikis, and support history, not the open internet. Deployed on your AWS account, so your data never leaves it.

  • Ingestion pipeline for your existing docs, wiki, and ticket history into a private vector knowledge base
  • Retrieval-augmented chat interface (Slack, internal web app, or embedded widget) with source citations
  • Access controls scoped to who should see what: no accidental cross-team data exposure
  • Handover with the ingestion pipeline documented, so new documents keep the assistant current

What that price covers Three weeks. Up to 500 documents from a single repository, one chat surface, and your existing single sign-on for access control.

What changes it

  • Document volume past 500, and how many separate repositories they live across
  • Additional chat surfaces: Slack and an embedded widget and an internal app is three builds, not one
  • Per-document or per-group access rules that go beyond your existing SSO groups
  • Multilingual content, which changes both the embedding strategy and the evaluation pass

AI Feature Integration for Existing Software

$12,000 – $20,000

Add real AI capability into the tool you already run: drafting, summarization, classification, or extraction, instead of replacing it with something new. Ships as a feature in your existing product, not a bolt-on app nobody opens.

  • Scoping session to pin down the exact task (summarize, classify, extract, draft) and where it lives in your current workflow
  • API integration into your existing application or internal tool, on your AWS account
  • Evaluation pass against real examples from your own data before go-live, not a generic demo
  • Full handover: you own the integration code and the API relationship, no ongoing dependency on us

What that price covers One task type, integrated into one application that already exposes an API we can write against.

What changes it

  • Each additional task type: summarize and classify are two features, priced as two
  • Legacy systems with no usable API, where the integration surface has to be built first
  • Throughput commitments: a feature handling millions of calls a month is an infrastructure problem, not just a prompt
  • Human-review workflows and audit logging, where a wrong answer carries real cost

AI, Excel & SQL Training for Teams

$3,500 per half-day

Hands-on training that turns your team into confident daily users of AI tools, Excel, and SQL, not another deck nobody opens.

  • AI tools training: prompt engineering, Claude/ChatGPT workflows, and safe day-to-day use of AI
  • Excel training: formulas and pivot tables through Power Query and dashboard-ready models
  • SQL training: querying, joins, and building the reports your team currently waits on someone else for
  • Custom curriculum built around your team's actual data and tools, not generic slides

What that price covers One half-day session, up to 15 seats, delivered remotely or at one Los Angeles-area site, using our existing curriculum adapted to your tools.

What changes it

  • Seats past 15, where the session stops being hands-on without a second facilitator
  • A curriculum built on your own data and systems rather than adapted worked examples
  • Multi-session series with exercises and follow-up review between sessions
  • Travel outside the Los Angeles area

Not sure which of these fits, or whether AI is even the right tool for the problem? Start with the readiness audit, it's built for exactly that question.