AI Agent Development Company for Operations, Support, and Internal Workflow

Most teams don't need a generic chatbot. They need an AI agent that does the work — pulls from the CRM, drafts the follow-up, files the ticket, updates the spreadsheet, and pings the right human when it's stuck. Revex is an AI agent development company that builds exactly that: custom AI agents for operations, customer support, and internal workflows. We run our own agent in production (Revex Brain) so we're shipping the same patterns we sell.

We don't compete on generic enterprise AI. We niche down. If you run a real estate brokerage, a services business, or any operations-heavy company drowning in repetitive work, this is the page for you.

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What a Revex AI agent build includes

An AI agent isn't a prompt. It's a system. Every Revex build includes the following components, scoped to your stack:

Agent core — the reasoning loop. Model selection (Claude, GPT, or a mix), system prompt, tool-call ceiling, and the persona/role definition.
Tool layer — the actions the agent can take: read from your CRM, write to Slack, query a database, send email, file a ticket, hit an internal API. We wire each tool with auth, rate limits, and audit logging.
Memory + context — short-term scratchpad plus persistent memory so the agent doesn't ask the same question twice. We use Supabase for storage and structured context assembly so the agent always has the right slice of your data.
Human-in-the-loop checkpoints — explicit handoffs where the agent stops and waits for a human. This is the difference between an agent that helps and an agent that breaks things in production.
Observability — every run logged: inputs, tool calls, model output, cost. You can replay any decision the agent made.
Deployment + ops — hosted on Railway, Vercel, or your cloud of choice. CI/CD, error monitoring, on-call runbook.

You own the code. You own the prompts. You own the model keys. Revex builds it, hands it over, and stays on retainer if you want help evolving it.

AI agent use cases we actually ship

We focus on three use case families because they have clear ROI and they're where AI agents genuinely outperform humans-plus-software:

1. Operations agents

Pull data from disparate systems, reconcile it, and produce the next action. Examples: a brokerage operations agent that pulls listing data from the MLS, checks compliance against state rules, drafts the disclosure packet, and queues it for human review. A finance ops agent that reconciles Stripe payouts against your accounting system and flags mismatches.

2. Customer support and AI chatbot development

An AI chatbot development company that only ships a chat widget is selling you 2022. Real support agents read the customer's account, check order status, file a refund, escalate to a human when sentiment turns, and write a summary into the ticket. We build the full stack — chat UI, agent backend, CRM integration, escalation logic — not just the bubble in the corner.

3. Internal workflow agents

The agents your team uses, not your customers. Examples: a sales agent that drafts personalized outreach from CRM signals. A research agent that monitors competitor sites and posts a weekly digest to Slack. A recruiting agent that scores inbound applicants against your rubric.

If your workflow is "human reads X, copies into Y, writes a follow-up in Z," it's almost certainly a candidate for an agent.

The Revex agent install process

We've run this process across our own product (Revex Brain) and client builds. It's tight on purpose — most AI projects fail because the scope drifts. Ours doesn't.

Discovery (week 1). We map the workflow you want automated: inputs, outputs, edge cases, who owns what today. Output is a one-page spec with a clear definition of done.
Tool inventory (week 1). We list every system the agent needs to read from and write to. Auth, rate limits, sandbox accounts. Anything that can't be accessed gets flagged before we write a line of code.
Build (weeks 2-4). Agent core + tool layer + memory + UI. We ship a working v1 inside a sandbox so you can poke at it before it touches production data.
Eval + tuning (week 4-5). We run a test set of real cases against the agent, score the outputs, and tune the prompt + tools until the success rate hits your bar.
Production rollout (week 5-6). Limited launch — one team, one workflow — with full observability. We watch the first week of runs together.
Handover. Code, runbook, prompts, eval set. You own everything. We stay on retainer if you want ongoing improvement.

Most builds land in the 4-6 week range. Bigger agent systems (multi-agent, multi-tool, high-throughput) go longer. We tell you which bucket you're in on the first call.

The Revex agent install process

We've run this process across our own product (Revex Brain) and client builds. It's tight on purpose — most AI projects fail because the scope drifts. Ours doesn't.

Discovery (week 1). We map the workflow you want automated: inputs, outputs, edge cases, who owns what today. Output is a one-page spec with a clear definition of done.
Tool inventory (week 1). We list every system the agent needs to read from and write to. Auth, rate limits, sandbox accounts. Anything that can't be accessed gets flagged before we write a line of code.
Build (weeks 2-4). Agent core + tool layer + memory + UI. We ship a working v1 inside a sandbox so you can poke at it before it touches production data.
Eval + tuning (week 4-5). We run a test set of real cases against the agent, score the outputs, and tune the prompt + tools until the success rate hits your bar.
Production rollout (week 5-6). Limited launch — one team, one workflow — with full observability. We watch the first week of runs together.
Handover. Code, runbook, prompts, eval set. You own everything. We stay on retainer if you want ongoing improvement.

Most builds land in the 4-6 week range. Bigger agent systems (multi-agent, multi-tool, high-throughput) go longer. We tell you which bucket you're in on the first call.

The Revex agent install process

We've run this process across our own product (Revex Brain) and client builds. It's tight on purpose — most AI projects fail because the scope drifts. Ours doesn't.

Discovery (week 1). We map the workflow you want automated: inputs, outputs, edge cases, who owns what today. Output is a one-page spec with a clear definition of done.
Tool inventory (week 1). We list every system the agent needs to read from and write to. Auth, rate limits, sandbox accounts. Anything that can't be accessed gets flagged before we write a line of code.
Build (weeks 2-4). Agent core + tool layer + memory + UI. We ship a working v1 inside a sandbox so you can poke at it before it touches production data.
Eval + tuning (week 4-5). We run a test set of real cases against the agent, score the outputs, and tune the prompt + tools until the success rate hits your bar.
Production rollout (week 5-6). Limited launch — one team, one workflow — with full observability. We watch the first week of runs together.
Handover. Code, runbook, prompts, eval set. You own everything. We stay on retainer if you want ongoing improvement.

Most builds land in the 4-6 week range. Bigger agent systems (multi-agent, multi-tool, high-throughput) go longer. We tell you which bucket you're in on the first call.

Revex Brain: our own AI agent in production

We don't recommend agent patterns we haven't run ourselves. Revex Brain is the AI agent system that runs inside Revex — a multi-persona agent stack where each persona (manager, SEO writer, engineer, GEO optimizer) has its own tool set, ceiling on tool calls, and model assignment. It writes Linear tickets, drafts blog posts, runs SEO research, and ships code reviews. It's wired into Slack, Linear, Webflow, Google Drive, GitHub, and SEMrush.

The same architecture — multi-persona, tool-scoped, observability-first — is what we ship to clients. When we say we know how to run agents in production, we mean we run one every day.