Generative AI stops at a draft: you ask, it writes, and a person still has to do everything around it. An AI agent takes the next step. It reads the incoming document, pulls the data it needs, decides what to do, and drafts the result into your tools, then routes anything uncertain to a person for approval. StartupLabs builds the agent around one expensive, repeatable workflow, with the review queue and audit trail built in. 5.0★ on Clutch, a US office in North Carolina.
Most “AI” on the market generates text and hands it back to you. That is useful, but the person still gathers the inputs, checks the systems, and does the follow-through. An agent runs the full sequence: read, look up, decide, draft into the system of record, and escalate the exceptions. The work moves, and your team reviews instead of doing.
The agent ingests the trigger (an email, PDF, form, or record), reads it, and pulls whatever else it needs from your systems and third-party sources.
It applies your rules, matches against your data, and drafts the next action, an order, a quote, a risk profile, a structured record, with the deterministic logic kept outside the model so the output is reliable.
Clean cases move; anything uncertain lands in a review queue for a person to approve or correct. Every step is logged for a full audit trail.
An agent handles the routine volume start to finish and surfaces only what needs a human judgment, so your team processes far more without more headcount, and nothing consequential happens without an approval.
These are live builds where software reads inputs, applies logic, and produces a structured result a person signs off on. Rated 5.0★ on Clutch.
Relevant builds: two automated underwriting engines → that gather data and score risk on a single input (income-assurance → is the second), the Quicklink Financial lead-engagement agent → that qualifies inbound leads over web and SMS, and IT BIDZ →, a B2B procurement platform with RFQ and quote workflows.
The agent reads an emailed, PDF, or faxed purchase order, matches SKUs, and drafts the order or quote into your ERP, flagging exceptions for a person.
Distributors playbook →Enter one identifier and the agent gathers the data, scores the risk, and assembles a sourced decision packet for an underwriter to approve.
Insurance playbook →Ingest documents, extract and structure the fields, and route the result into the right system, with every value traceable to its source.
Document AI →Multi-step research, reconciliation, and record-keeping tasks that today move between people and spreadsheets, run by an agent with a human reviewing the exceptions.
Workflow automation →A chatbot answers a question and stops. An agent completes a task: it gathers the inputs, applies your rules across several steps and systems, and produces a finished action a person approves. You get the workflow done, not just a draft to work from.
Only where you let it. We set the boundary with you: routine, low-risk cases can complete automatically, and anything above that threshold routes to a review queue for a person to approve. Nothing consequential happens without a human, and every action is logged.
We keep the deterministic logic, your rules, thresholds, and calculations, outside the language model, and use the model for the reading and drafting it is good at. That, plus the human-review step and the audit trail, is how the output stays trustworthy in production.
We start with a fixed-fee audit that proves the agent on your real inputs. Production builds generally run $40,000–$150,000 depending on scope, and the audit is credited toward the build.
We're a senior-led studio with a US office in North Carolina, 100% Upwork Job Success, 5.0★ on Clutch, and live agents in production. You work directly with the senior-led team, founder included, with a 4–8 hour response time.
Book a 15-minute call. We'll pick the workflow worth automating end to end and tell you what it'd take.
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