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Agentic sales and inventory operations is an AI agent layer that takes inbound sales requests from your messaging channels, checks them against verified inventory, asks a person for authorization where your rules require it, executes the approved workflow and tracks fulfilment to completion.
Enso Labs is a principal-led AI managed-services studio in New York City. We design that layer around your rules, build it against your systems of record, and operate it in production.
This page is written for a business-development or operations director at a mid-market retailer or brand whose sales requests arrive in conversation, whose stock lives in more than one system, and whose approvals happen in chat threads.
Customers and sales staff send requests through messaging apps, email and direct messages. Someone reads each one and retypes it into another system.
Stock sits across an ERP, a point-of-sale system, locations and spreadsheets. Answering “do we have it, and where” takes a person and several lookups.
Pricing, allocation and reservation decisions need sign-off. Today that sign-off is a reply in a chat thread that nobody can audit later.
The agent layer sits between your channels and your systems of record. It does the lookups and the routine steps, and it stops for a person wherever your rules say it must.
The agent reads each inbound request from the connected channels, extracts the item, quantity, customer and deadline, and asks a clarifying question when something is missing.
It queries your inventory system of record through a typed connector at the time of the request. It does not answer from memory. If records are stale or disagree, it says so and escalates.
Your rules define what the agent may do alone and what needs a named approver. The approver receives the request with the evidence attached and can approve, decline or edit it.
After approval the agent runs the agreed steps in your systems, for example reserving stock, creating the order or quote, and confirming with the customer and the team.
It follows each order through to completion, flags delays and exceptions to the owner, and closes the loop with the customer.
Every request, lookup, approval and action is logged. A test set built from your real past requests is run before any change goes live.
Maturity assessment, opportunity map, prioritized backlog. It ends with a written roadmap and a working agentic prototype.
Here the audit maps your request channels, inventory sources and approval rules, and the prototype handles real request examples against a copy of your data.
Scoped to one high-leverage use case: roadmap, business case and governance, a production agentic system, an eval harness and an ops runbook.
The pilot puts one request type into production end to end, with the authorization step and the audit trail in place from the first day.
We run the system, report on it monthly, and train your people on it. When you want it in-house, we document it and hand it over.
More request types and channels are added once the first one is stable and your team trusts the approvals.
Our published case studies are in other sectors. They use the pattern this service depends on: verified data, explicit rules, a person on approval, and a logged action.
A 9-rule expert knowledge encoding system for a Fortune 500 manufacturer, with 8 LangGraph stages, Claude-powered relevance ranking and a scientist-in-the-loop trust architecture.
Read the case study →Our own autonomous multi-agent trading platform, with MCP-connected brokerage APIs. We operate it as proof that our agentic architecture works under real market conditions, not just in client demos.
Read the case study →How we build agent systems: custom MCP servers that connect agents to internal APIs and databases, eval harnesses, guardrails, audit trails and human-in-the-loop checkpoints.
See the build practice →It is a set of AI agents that sits between your sales channels and your systems of record. The agents take inbound sales requests, check verified inventory, ask a person for authorization where your rules require it, execute the approved workflow and track fulfilment. Enso Labs designs, builds and operates this layer as a managed service.
On the intake side, messaging channels, email and web forms, subject to what each platform’s business API allows. On the operations side, inventory, order and CRM systems that expose an API or a reliable export. We confirm what each of your systems allows during the 2-Week AI Audit, before any build is scoped.
The agent only reports what it reads from your inventory system of record at the time of the request, and it shows the source it used. It does not estimate. When records are missing, stale or in conflict, it says so and routes the request to a person instead of answering.
You decide. The rules are written down during the audit, for example by order value, customer, item or discount. The agent cannot execute a step that falls under those rules until a named approver has approved it, and every approval is logged with who approved and what they saw.
It stops and hands the request to a person with the full context. Every deployment includes audit trails, human-in-the-loop checkpoints and access controls, and changes are tested against a set of real past requests before they go live.
In three stages. A fixed-fee 2-Week AI Audit, scoped before it starts, maps the workflow and delivers a working prototype. A 12-Week Pilot-to-Production takes one request type into production with governance, an eval harness and an ops runbook. After that Enso Labs runs the system as managed operations and reports on it monthly. You own the custom code, prompts, eval suites and documentation.
Tell us where requests come in, where inventory lives and who approves. Sav replies within 24 hours.
