Automated Intelligent Document Extraction in Professional Services

AI-native extraction uses Professional Services business logic - it reads SOW terms, client hierarchies, and engagement metadata from your Maconomy, Workday PSA, or Deltek Vision instance to classify and validate extracted hours, expenses, and project codes - targeting 95%+ accuracy - not just convert images to text. The system understands that a "Senior Manager" timesheet on a fixed-fee engagement requires different handling than T&M billing, recognizes client entity aliases, and automatically flags potential scope creep by comparing extracted actuals against contractual SOW limits. This context-aware approach eliminates the manual re-validation and re-keying that generic OCR tools require.

Yes. Documents and extracted data stay inside your systems - the extraction layer reads from and writes to your Maconomy, Workday PSA, or Deltek Vision instance, and every extraction and correction action is logged with a full audit trail for regulatory review. For Professional Services compliance specifically, the system enforces SOX cost allocation rules for public company clients, respects SEC independence time-coding restrictions for audit engagements, and validates IRS Circular 230 CPE hour tracking for tax advisory firms - built to support your firm's compliance obligations, not to replace your own compliance sign-off.

Plan for a working system inside the first 100 days: weeks 1-2 cover system architecture and integration planning with your Maconomy, Workday PSA, or Deltek Vision team; weeks 3-6 involve training the extraction model on 200-300 of your historical documents and tuning business rule logic; weeks 7-9 cover UAT and operations team training; go-live occurs in week 10. A rollout like this is scoped to show measurable results - recovered utilization and faster proposal turnaround - within 60 days of go-live as the system processes your first full billing cycle.

The benefits land in three places you can measure: utilization, margin protection, and speed. The working target is 3-5 percentage points of utilization recovered within 90 days once billable hours stop sitting in an entry backlog. Real-time actuals tracked against SOW budgets catch scope creep while a project can still be adjusted, instead of at final billing. And proposal turnaround is targeted to improve 40-50% because historical engagement data is retrievable in seconds rather than pulled manually from old files - all scoped against your own volumes during the assessment.

Partly, and it depends on how much of your book looks like that. The 85%+ high-confidence auto-post rate assumes the model has been fine-tuned on your firm's own historical SOWs and document templates. Firms serving large enterprise clients who impose their own timesheet and expense formats will see lower initial confidence scores and higher exception volume on those engagements until the model calibrates - budget a 60-90 day window where operations corrections actively feed the retraining loop before judging steady-state throughput.

It is built for firms running real document volume across Maconomy, Deltek Vision, or Workday PSA - typically 50+ person shops where a few points of recovered utilization or a materially faster proposal cycle pencils out against the integration cost. Smaller shops with low engagement volume and simple, largely manual timesheet and expense processes may not recover the build cost as quickly; a lighter operational fix will likely get there faster for that profile.

Accuracy improves because the system runs three specific checks at intake instead of after the fact: it allocates costs to the correct project code and cost center, flags scope creep by checking billed hours against SOW contractual limits in real time, and tracks CPE hours for CPAs against state licensing requirements. Catching these at the point of extraction - rather than during month-end close or an audit - is what removes the re-validation cycle generic OCR tools leave behind.