Run the back-office workflow end to end, not just a demo of it.
We build AI automation that does the repetitive, document-heavy work your team does by hand: read the invoice, classify the claim, check it against your rules, write it into your systems, and route only the real exceptions to a person.
Deployed where the volume is — accounts payable, claims, onboarding, order processing, compliance review — inside your own cloud, measured for accuracy before it touches a live transaction. Fixed scope, one accountable lead, production in 4–8 weeks, every model and line of code assigned to you.
Because the work is messy in exactly the way old automation couldn’t handle. The classic rules-based bot (RPA) follows a fixed script and breaks the moment a vendor changes an invoice layout, a form arrives as a scanned PDF, or a request is phrased in a way nobody anticipated.
So the high-volume, high-cost processes — reading documents, reconciling data, approving routine cases — stay manual, slow, and error-prone.
AI changes what’s automatable. A model can read an invoice it has never seen, understand a free-text email, and extract structured data from an unstructured document — the steps that defeated rules-based automation.
McKinsey Global Institute’s analysis of 2,000+ work activities found that about 50% of the activities people are paid to do are technically automatable with currently demonstrated technology, while fewer than 5% of jobs can be fully automated (McKinsey Global Institute, A Future That Works, 2017). The honest reading matters as much as the opportunity: this automates tasks within a job, with people kept on the exceptions.
This isn’t one product; it’s a pattern applied to a handful of specific, high-volume, document-and-rule-heavy processes. For each: what it does, the benefit it produces, and a concrete example of the help.
Reads invoices in any format, matches them to purchase orders and receipts, flags discrepancies, and queues clean ones for payment. Benefit — lower cost per invoice and faster cycle time, with fewer errors.
For example, a supplier’s PDF invoice is read, three-way-matched, and approved within minutes — while the one invoice whose amount doesn’t match the PO is the only one a clerk ever sees.
Extracts data from claim forms, supporting documents, and emails, validates it against policy rules, and auto-adjudicates the straightforward cases. Benefit — shorter turnaround and consistent decisions, with capacity freed for complex cases.
For example, a clean, in-policy claim is settled the same day instead of waiting in a multi-day queue, while edge cases are routed to an adjuster with the data already extracted.
Turns unstructured documents — contracts, forms, statements, scanned paperwork — into structured, validated records in your systems. Benefit — eliminated manual keying and a lower data-error rate.
For example, fields from a 30-page contract are extracted, validated, and written straight into the system of record, so a task that took an analyst an afternoon takes minutes and the typos never enter the data.
Collects and verifies documents, provisions accounts, and moves a new hire or account through each step of an approval chain. Benefit — faster time-to-productive and a consistent, audited process.
For example, a new hire’s paperwork is verified and their accounts requested automatically the moment HR marks them accepted, instead of a coordinator chasing five systems by hand over a week.
Screens documents and transactions against policy and regulatory rules, surfacing the items that need human judgment with the evidence attached. Benefit — broader coverage and a complete audit trail, with reviewers focused on real risk.
For example, every contract is checked against the required clauses and only the non-conforming ones are escalated — clause highlighted — instead of a sampled manual review that misses the rest.
Processes incoming orders, change requests, and status updates from email, portals, and documents directly into your order and ERP systems. Benefit — lower processing cost and fewer fulfillment errors from manual handling.
For example, an emailed bulk order is parsed and entered into the ERP automatically, removing the re-keying step where a wrong quantity used to slip in.
The scope below is the difference between automation that scales and a pilot that quietly stalls.
We map your candidate workflows, measure the real volume and cost, and rank them by payback — run as our AI readiness assessment, with the honest “this one isn’t worth automating yet” call included.
The intelligent layer that reads invoices, forms, contracts, and emails — including scanned and unstructured ones — and turns them into structured, validated data. Extraction accuracy is measured against your own documents before anything goes live.
We encode your business rules and route each item: auto-process the clear cases, hold the ambiguous ones, escalate the rest. The decision logic is explicit and inspectable — not a black box your auditors can’t follow.
We wire the automation into your ERP, finance, CRM, and ticketing systems through governed, permissioned connections, so it reads and writes inside the access controls your security team already runs — never bolted on outside them.
Human-in-the-loop review is designed in, not retrofitted: below a confidence threshold the item goes to a person, with the extracted data and the reason for the flag attached, and that correction feeds back to improve the model.
We instrument accuracy, throughput, and exception rate, watch for drift as your documents and rules change, and train your team to read the dashboards, handle exceptions, and own the system.
What you get when you hire us — all assigned to you
The same delivery model behind all our AI development work, tuned for process automation — one accountable lead, fixed scope, no handoffs.
Pick the workflow, measure its real volume, cost, and error rate, and define the success metrics we’ll be judged on.
Output: a ranked plan & a baseline to beat
Build the accuracy test set from your real documents and cases, design the extraction and decision logic, and set the confidence threshold for human escalation.
Output: a golden test set & a target straight-through rate
Develop the pipeline in your own cloud tenant, wired to your systems through governed integrations, with exception routing and the audit trail in place.
Output: a working automation behind your access controls
Shadow mode first, then a controlled pilot, then full volume — accuracy and exception rate measured weekly, your team trained to operate it.
Output: a production automation & a team that owns it
Most engagements reach production in 4–8 weeks, with full work-for-hire IP assignment signed at kickoff.
AI automation touches invoices, claims, and records that move real money, so the production discipline underneath it is the whole game. That discipline is what we’ve shipped for more than a decade across very different stakes:
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We’ll tell you plainly when a workflow isn’t worth automating yet — which a vendor paid by the bot won’t.
Built to scale, not to demo. Deloitte’s data shows the cost savings land only when automation is scaled into production — and shipping reliable production systems, not slideware pilots, is exactly what the Aegis AI discipline is for.
Auditable by design. Decision rules are explicit and every action leaves a trail — so your finance, risk, and compliance teams can follow what the automation did and why, instead of trusting a black box.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes the workflow answers for it in production.
Built to transfer. Models, rules, integrations, and code are assigned to you under full work-for-hire IP assignment; your team is trained to run and extend the automation when we step back.
Invoice processing, KYC document review, and transaction reconciliation, every step audit-logged and rules-explicit. Fintech software →
Claims, prior-authorization, and intake-document processing inside HIPAA-compliant architectures, with humans on every clinical-judgment exception. Healthcare software →
Claims intake and adjudication, policy-document extraction, and submission triage, with the straightforward cases auto-handled and the complex ones routed with evidence attached.
Supplier-invoice processing, order entry, and store-paperwork digitization across locations, standardized and measured.
What teams want to know before they put an AI automation on a live workflow.
RPA follows a fixed script and breaks on anything it wasn’t programmed for — a new invoice layout, a scanned PDF, a free-text email. AI automation adds an intelligent layer that reads and understands unstructured input, then uses your rules to decide and act. In practice we often keep your RPA for the deterministic last mile and put the AI in front of it to handle the messy input.
Measurement and a confidence threshold. We build an accuracy test set from your own documents and cases, measure extraction and decision accuracy against it before anything touches production, and run in shadow mode first. Anything below the confidence threshold goes to a person rather than auto-processing, every action is logged for audit, and we monitor accuracy after launch as your documents and rules drift.
It takes over the rote portion of the work — the keying, matching, and routine approvals — and routes the judgment cases to your team with the data already prepared. McKinsey’s analysis finds fewer than 5% of jobs are fully automatable; the realistic outcome is people freed from rote work for higher-value tasks, not eliminated. We scope it as task automation with humans on the exceptions, not headcount removal.
An AI agent decides its own steps toward an open-ended goal; AI automation runs a defined workflow reliably with a human on the exceptions — bounded, auditable, and lower-risk, which is what most back-office processes actually need. When a problem genuinely calls for autonomous decision-making, that’s our agentic AI development work, and we’ll tell you which one your process needs.
The automation runs in your own cloud tenant under your access controls; integrations use scoped, permissioned connections to your systems of record; and every engagement starts with an NDA and a security review. Business API traffic to the major model providers isn’t used to train their models by default, and we document every data path so your team verifies rather than trusts.
You do — completely. The extraction models, decision rules, integrations, and code transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend it. Keep us on a reduced retainer or take the keys; the engagement is built around the handover.
Most automations reach production in 4–8 weeks under a fixed-scope engagement with one accountable lead. Build cost depends on the workflow’s complexity — our AI development cost guide gives real ranges — and payment is tied to the ROI we scope against your baseline.
Thirty minutes · No pitch deck
Bring the process that’s eating the most hours — we’ll tell you honestly whether AI automation fits it, what it takes to build, and what it saves against your current cost.