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, and pings a human when stuck. Revex builds custom AI agents for operations, support, and internal workflows.
Pull data from disparate systems, reconcile it, and produce the next action. Example: a brokerage ops agent that reads listing data from the MLS, checks state-rule compliance, drafts the disclosure packet, and queues it for human review. Or a finance ops agent that reconciles Stripe payouts against your accounting system and flags mismatches.
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 back into the ticket. We build the full stack — chat UI, agent backend, CRM integration, escalation logic.
The agents your team uses, not your customers. 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 Z," it's a candidate.
Primary reasoning model with tool use, structured outputs, and a tool-call ceiling per persona.
Persistent agent memory, run history, and structured context assembly so the agent never asks twice.
Where humans and agents hand off. Tickets, escalations, and async approvals built into the loop.
Production deploys with CI/CD, error monitoring, and per-run observability — replay any decision.
Every Revex agent build ships the same six pieces, scoped to your stack. You own the code, the prompts, and the model keys.
Reasoning loop, model selection, system prompt, tool-call ceiling, and persona definition.
CRM reads, Slack writes, database queries, ticket filing — each tool wired with auth, rate limits, and audit logging.
Short-term scratchpad plus persistent memory in Supabase so the agent never asks the same question twice.
Explicit handoffs where the agent stops and waits for a human. The difference between an agent that helps and one that breaks production.
Every run logged — inputs, tool calls, model output, cost. Replay any decision the agent made.
Hosted on Railway, Vercel, or your cloud of choice. CI/CD, error monitoring, on-call runbook handed over.
Common questions about hiring an AI agent development company
A working agent: model + system prompt + tool layer + memory + UI + observability + deployment. Not a chatbot, not a Zapier flow. Something that can take an input, decide what to do, use tools to do it, and report back. Revex builds the full system and hands you the code.
A chatbot answers questions. An agent takes actions. We build chatbots too — but with agent backends. The chat UI is the cheap part. The hard part is making the thing on the other side actually useful.
Most v1 builds land between $20k–$60k depending on tool count and eval rigor. We quote on a one-pager spec, not a vague brief. Book a call and we'll scope it on the spot.
Yes. We're tool-agnostic on infra (Railway, Vercel, AWS, GCP), CRM (HubSpot, Salesforce, Pipedrive, custom), and model provider (Anthropic, OpenAI, mixed). The agent pattern doesn't care.
You do. We hand over the repo, system prompts, eval set, and runbook on day one of production. No lock-in.
4–6 weeks for most v1 builds. Bigger multi-agent systems go 8–12 weeks. We commit to a date on day one and ship to it.
