Web · API · MCPReal estate · CRE
Relm: multi-agent AI that finds hidden deal risk in commercial real estate
Minutesaddress to Intel Report
AI Agent Development
AI agents that do the work, not the demo.
We design and build autonomous AI agents and multi-agent systems that read, decide and act across your tools, with human approval where it matters.
2 wks
to a working prototype
Fixed
price after discovery
100%
your code and IP
What you getEverything to go from idea to production.
01
Multi-agent orchestration
Specialist agents for research, extraction, decisions and actions, coordinated by one controller.
02
Tool & API integration
Agents read and write to your CRM, ERP, databases, email and internal APIs.
03
Human-in-the-loop
Approval steps, escalation rules and full audit logs for every action.
04
Evaluation suite
Real-world test sets that score accuracy before and after every change.
05
Guardrails & security
Permissioning, PII redaction and zero-retention model endpoints.
06
Monitoring
Dashboards for cost, latency, success rate and drift in production.
How we deliver
- STEP 01
Discover
Map one workflow and one metric.
- STEP 02
Prototype
Agent running on your real data.
- STEP 03
Harden
Evals, guardrails, integrations.
- STEP 04
Launch
Ship, measure, expand.
Stack by category
Models
- Anthropic
- OpenAI
Agents
- LangGraph
- MCP
Languages
- Python
Model hosting
- AWS Bedrock
- Azure OpenAI
Data
- Postgres
Industries
Build or buyCustom agent or off-the-shelf?
Off-the-shelf agents start faster. Custom agents win when the work crosses systems or carries real risk.
| Your situation | Our recommendation |
|---|---|
| The task lives inside one SaaS tool, like drafting helpdesk replies | Buy the vendor’s built-in agent |
| The workflow crosses your CRM, ERP, email and internal APIs | Build a custom agent |
| Actions move money, change records or reach customers | Build custom, with approvals and audit logs |
| You’re not yet sure the use case pays off | Start with a 2-week Discovery Sprint |
Our recommendation: buy for single-tool tasks; build when the agent runs a workflow you want to own and measure.
Typical engagementWhat an AI agent costs and how long it takes.
- 01 · 2 weeks
Discovery Sprint
$5,000fixed fee · 2 weeksOne workflow mapped, one success metric agreed, and a prototype agent running on your real data.
- 02 · 6–16 weeks
Fixed-Price Build
$25k–$120kper project · 6–16 weeksA production agent with integrations, evaluation suite, guardrails, approval steps and monitoring.
- 03 · Ongoing
Dedicated Team
From $12kper month · cancel anytimeNew agents, tools and model upgrades on a monthly roadmap, with weekly demos.
What moves the price: how many systems the agent touches, how many actions need human approval, and how much evaluation data already exists. Compare engagement models
Selected workShipped with this.
Web · API · MCP
Web platformAffiliate & creator CRM
Superdeal: an AI agent that runs creator campaigns end to end
3platforms: Instagram, TikTok, YouTube
GuidesRead before you build.
- Buying software · 9 min readAI agent development cost in 2026: real price ranges
- AI strategy · 8 min readBuild vs buy AI agents: a CTO decision framework
- AI strategy · 7 min readHow to evaluate an AI agent development company: a checklist
- AI strategy · 5 min readWhy most AI pilots never reach production — and how to be the exception
FAQCommon questions.
What’s the difference between a chatbot and an AI agent?
A chatbot answers questions. An agent takes actions, like updating records, sending emails or making decisions, within the rules you set.
Which models do you use?
We stay model-agnostic and pick per task, from Anthropic, OpenAI, Google or open-source, so you can switch as the market moves.
How do you stop agents making mistakes?
Evaluation sets, confidence thresholds, approval steps for high-impact actions, and a full audit log.
How much does it cost to build a custom AI agent?
Most engagements start with a 2-week Discovery Sprint ($5,000 fixed fee, credited to your build), followed by a fixed-price build ($25k–$120k per project, 6–16 weeks) or a dedicated team (from $12k per month). The build price is fixed after discovery and billed in milestones tied to demoed features. Model usage is a separate running cost, paid to the model provider from your own account, which we estimate in discovery.
How long does it take to build an AI agent?
A prototype agent running on your real data takes about two weeks, inside the Discovery Sprint. A production agent with integrations, evaluations, guardrails and monitoring is a fixed-price build of 6–16 weeks, depending on how many systems it touches.
Which frameworks and tools do you build agents with?
Usually Python with LangGraph for orchestration, MCP for tool access, and models from Anthropic, OpenAI or Google, called directly or through AWS Bedrock or Azure OpenAI. Postgres holds agent state and audit logs. The model stays swappable.
Can the agent work with our CRM, ERP and internal tools?
Yes. Agents call your systems through their APIs or through MCP servers we build, with permissions scoped per action. Relm, for example, exposes 44 tools over REST and MCP so other agents can use it.
Who owns the agent, the code and our data?
You do. Code lives in your repository from day one, the IP is yours, and the agent runs in your cloud or on endpoints you control. Model calls go through business API terms that exclude training on your data, with zero data retention where the provider offers it.
What maintenance does an AI agent need after launch?
Agents need monitoring and regular re-evaluation, because models, prompts and your data change. Every build includes 30 days of support; after that, a monthly retainer or dedicated team covers monitoring, eval runs, model upgrades and new tools.
Should we build a custom agent or buy an off-the-shelf one?
Buy when a vendor’s agent already covers the task inside one tool, such as the assistant built into your helpdesk. Build custom when the workflow crosses several systems, needs your own rules and approvals, or is a process you want to own and measure.
