PROPTURE, system 02
Property operations platform
Runs property operations, with voice agents on the phones.
- Replaces
- Front-desk calls and maintenance coordination
Custom AI agent development for your business
Agents for the steps that need reading and judgment, with every action logged and exceptions routed to your team.
What does an AI agent development company do?
An AI agent development company designs, builds and maintains software agents that do multi-step work: reading an email or document, checking it against your rules, updating your ERP or CRM, and escalating what falls outside policy. We build custom AI agents for business processes, test them on your past cases, and run them with limits and an audit trail.
Agent trace · Refund request 48213
Illustrative- 01180 ms
toolcrm.get_customer(email)
Customer since 2019 · 0 prior refunds
- 02240 ms
toolorders.lookup("48213")
$1,240.00 · 2 items · delivered 3 Oct
- 03410 ms
toolpolicy.search("damaged refund")
Returns policy §3.2 · 2 passages
- 0412 msDone
checkvalidate(refund_request)
Amount matches order · inside 30-day window
- 053 ms
guardlimits.refund(1240.00)
Refund above $500 limit → routed to ops lead
- 06290 ms
toolslack.post("#ops-approvals")
Approval request sent with summary and sources
Stopped before acting · 6 steps in 1.1 s · every call, input and source kept in the log
The problem
Every process has steps that need reading and judgment: understanding an email, checking a document against a policy, deciding who should handle a request. Those steps keep skilled people stuck on routine work.
Our approach
We map the workflow with the people who run it, then build agents that take it over. They read documents, extract and check the data, update your systems of record and send a person anything that falls outside the rules.
- Manual data entry removed from the process
- Every action recorded and reviewable
- More volume handled by the same team
How one run works
Each step is either fixed logic, a model doing the reading or drafting, or a person on your team. You decide which steps wait for approval.
Intake · intake agent
Reads the document or request
Extract · parser agent
Structures and validates the data
Update · ops agent
Writes to your systems of record
Review · your team
Only exceptions reach a person
AI agents · one run
Illustrative- IntakeReads the document or requestIntake agent
- ExtractStructures and validates the dataParser agent
- UpdateWrites to your systems of recordOps agent
- ReviewOnly exceptions reach a personHuman in the loop
Manual data entry removed from the process
What we build for you
We map the workflow with the people who run it, then build agents that take it over.
- 1
Evaluation before launch
A test set built from your past cases, scored field by field, and re-run on every change.
- 2
System updates
Agents work in your ERP, CRM and internal tools through APIs, or a controlled browser where no API exists.
- 3
Exception handling
Clear rules for what an agent decides alone and what goes to a person, with the context attached.
- 4
Limits and guardrails
Scoped credentials, per-action limits and checks on untrusted input such as inbound email, enforced in code outside the model.
Related systems in production
Your project starts from architecture we already run.
IP Sentinel, system 03
Legal operations system
Monitors USPTO activity and drafts office-action responses for review.
- Replaces
- Docketing and first-draft preparation
Your workflow
A system designed for the function you want to automate next, built on the same architecture as the ones above.
Discuss your workflow
Questions about AI agent development
Used in
Part of these end-to-end blueprints
How much does AI agent development cost?
It depends on how many systems the agent touches, how much judgment the task needs and the volume it handles. We price the first agent as a fixed scope after a one-to-two-week workflow audit. Running costs are model usage, hosting and monitoring, and we estimate them per task before you commit.
Can we build an AI agent on our own?
A prototype, yes. Current frameworks make a demo agent a short project. Production is the hard part: a test set from real cases, scoped permissions, error handling, monitoring and someone on call when it breaks. Teams with engineers to spare often build in-house; we help the ones who would rather not staff that.
What is agentic AI development, and when do you need it?
Agentic AI means a model that plans steps and calls tools, such as a CRM lookup or a refund API, instead of only answering a prompt. You need it when the task changes from case to case. For fixed sequences, plain workflow automation is cheaper and easier to audit, and we will say so.
What is multi-agent AI?
Multi-agent AI splits one job across several agents, each with its own role, tools and permissions. For example, one agent reads incoming invoices, another checks them against POs and a third drafts the approval request. It helps when one agent’s instructions would get too long or when steps need different access. It also adds hand-offs that can lose context, so we start with one agent and split only when tests show a single agent failing at a specific step.
Which frameworks and models do you use?
LangGraph, LlamaIndex or a custom orchestrator, with OpenAI, Anthropic, Google Gemini or open models such as Llama, Mistral and Qwen, chosen per task and swapped only after the evaluation suite passes.
What if our tools have no API?
We use controlled browser automation for those steps, with the same logging, permissions and review as any other action.
How are mistakes handled?
Each step validates its output. A case goes to a person when validation fails, no supporting source is found, a policy rule triggers (amount, customer tier, record type), or the customer asks for one.
How do you test an agent before it goes live?
We build a test set from your past cases with the correct outcome for each, then score the agent on it: accuracy per field or decision, how often it escalates, and how often it is wrong without noticing. It then runs in shadow mode on live work before it acts on its own, and the test set is re-run on every change.
Guides on AI agent development
All articlesWhatsApp chatbot for business: API access, pricing model and what the bot should hand off
A WhatsApp chatbot for business runs on Meta's Business Platform. Learn how the 24-hour window, templates and per-message pricing work, and what to hand off.
8 min read
n8n vs Zapier vs Make: how to choose a workflow automation tool
n8n vs Zapier vs Make compared on hosting, pricing model, code, AI features and limits, with a same-workflow cost example and when custom code wins.
8 min read
AI agent development cost: what to budget to build and run an agent
The cost to build an AI agent depends on tools, autonomy, testing and token use. See the cost drivers, three complexity tiers and a worked per-run example.
8 min read
Other solutions
Knowledge assistants
Search and answer across your documents, tickets and databases, with sources and access rules.
Private AI models
Small models trained on your domain and deployed in your environment for private, high-volume work.
Workflow automation
End-to-end automation of multi-step processes across your CRM, ERP, email and internal tools.
Tell us which step needs reading and judgment.
A 30-minute call with an engineer. You will leave knowing what a system could do for that workflow and what it would take to build.
What happens next
- 1
Strategy call
We learn how the workflow runs today and what a good outcome would be.
- 2
Workflow audit
We map the process and measure where a system would save the most time.
- 3
Proposal
A written scope, timeline and fixed price for your first system. No obligation to build.