— The business case —
You're paying people to do work machines should handle.
Most $5M–$15M businesses have the same pattern: a handful of workflows that cost 5–20 hours of staff time per week, done manually because no one has had time to automate them. Ticket triage. Lead qualification. Document review. Data extraction. Report generation. These are exactly the tasks AI agents are built for.
The problem isn't the technology — it's that most AI development for SMBs delivers chatbots and demos, not production systems. We build agents that connect to your real tools, run evals before they touch customers, and come with the observability to catch problems before you do.
— What we build —
Four types of agents, one production standard.
Every agent is scoped to a specific workflow. We don't build general-purpose bots — we build agents that own a defined task end-to-end.
Operations agents
Workflow automation across your back office.
Routes, transforms, and acts on structured data across your tools.
- Approval routing + escalation paths
- Data sync between disconnected systems
- Report generation + distribution
Support agents
Tier-1 resolution without the queue.
Handles inbound queries, resolves what it can, escalates what it can't — with full context.
- FAQ + knowledge base resolution
- Ticket triage + priority scoring
- CRM lookup + response drafting
Sales agents
Lead qualification that runs while you sleep.
Qualifies inbound leads, scores against your ICP, and hands off to sales with context.
- ICP scoring + enrichment
- Outreach drafting + sequencing
- CRM update + handoff memo
Data agents
Extract signal from unstructured input.
Reads documents, emails, and forms — extracts structured data and takes action on it.
- Contract + invoice extraction
- Email classification + routing
- Form processing + validation
— What's included —
Every build includes four non-negotiables.
Agent design + tool use
We scope the agent's capabilities, the tools it can call, the data it can read, and the actions it can take. The design is approved before code is written.
Evaluation suite
A set of test cases that run against the agent before every deployment. If the agent fails any eval, it doesn't ship. You get the eval results as part of the handoff.
Observability layer
Every LLM call is logged with inputs, outputs, latency, and cost. You get dashboards your team can read without touching code. Drift and failures surface automatically.
Production deployment
Auth, rate limiting, error handling, and rollback gates — not a Jupyter notebook. Deployed to the same infrastructure as the rest of your stack.
— How it works —
Five steps, eight weeks.
Discovery
We learn your ops, tools, and the workflow you want to automate. Output: a written scope doc.
Architecture
Tool map, data flow, agent type selection, eval criteria. You sign off before build starts.
Build
Agent implementation, tool integrations, internal testing. Weekly update sent.
Eval
Eval suite runs. Agent tested against real inputs. Issues fixed before touching production.
Deploy
Production deployment with observability, docs, and a 30-min handoff call.
— Built for —
Right for some. Not for everyone.
Built for
- $1M–$15M businesses with real digital operations
- Workflows costing your team 5+ hours/week
- At least one technical contact on your team
- Comfortable running production AI without hand-holding
Not a fit
- Pre-revenue or <$1M — the ROI math doesn't work yet
- Projects with undefined requirements or moving scope
- Businesses that need a vendor to manage the AI after delivery
- Regulated industries (healthcare, finance) requiring compliance expertise we don't carry
Start with the audit or book a call.
Most engagements start with the $497 AI Profit Leak Audit — it identifies the highest-ROI agent opportunity in your operations and produces the spec we'd build from. Skip the audit if you already have a defined scope.
— Common questions —
Quick answers.
What is AI agent development?+
AI agent development is the process of building software systems that use large language models (LLMs) to autonomously complete multi-step tasks — calling APIs, reading data, making decisions, and taking actions without constant human input. Unlike a simple chatbot, an AI agent can handle workflows like triaging support tickets, qualifying leads, or orchestrating back-office operations end-to-end.
How long does it take to build an AI agent?+
Most production-ready AI agents take 4–8 weeks from scoping to deployment. Week 1–2 covers requirements, tool design, and eval criteria. Weeks 3–5 are build and internal testing. Weeks 6–8 are integration, observability setup, and production rollout. Timeline depends on complexity and how many external systems the agent needs to connect to.
How much does AI agent development cost?+
Custom AI agent projects with Metageeks start from $8,000 for single-agent systems with 2–3 tool integrations. Multi-agent systems with full eval pipelines and observability are typically $15,000–$40,000. All engagements are fixed-scope with defined acceptance criteria — no open-ended retainers.
What's the difference between an AI agent and a chatbot?+
A chatbot responds to messages. An AI agent acts on them. Agents have tools (APIs, databases, file systems), memory across steps, and the ability to orchestrate multi-step workflows. They can read your CRM, draft a reply, update a record, and send a notification — all in one run. Chatbots can't do any of that without human intervention at each step.
Do you use OpenAI, Anthropic Claude, or something else?+
We are model-agnostic and select based on your use case. GPT-4o is strong for structured data extraction and tool use. Claude is excellent for long-context reasoning and policy-adherent outputs. We also use open-weight models (Llama, Mistral) for latency-sensitive or on-premise deployments. The right model is part of the architecture decision, not a preset.
What's included in observability and eval?+
Every agent we ship includes LLM call logging, latency and cost tracking, and a baseline eval suite that runs on each deployment. You can see what the agent did, why it did it, and whether it's drifting. We use LangSmith or a comparable tracing layer — your team can access dashboards without needing to read code.
— Ready to start? —
Build the agent. See what it saves.
Most clients run the first agent in production within 8 weeks. If you already know the workflow — book a call. If you need to find the right one first — start with the audit.