| Criteria | Manual | Arctis AI |
|---|---|---|
| Time per trade package (3-5 bidders) | 2-5 working days | 30-60 minutes |
| Time per project (10+ packages) | 4-8 weeks | 1-2 days |
| Document reading | Manual, selective (time pressure means attachments skipped) | Complete — every page of every attachment analyzed |
| Bid normalization | Reviewer's mental model + spreadsheet | Algorithmic — consistent rules applied to all bidders |
| Coverage of tender requirements | Partial — depends on reviewer thoroughness | 100% — every requirement checked against every bid |
| Risk identification | Depends on reviewer experience and time | Systematic — trained on construction risk patterns |
| Consistency across packages | Varies by reviewer, fatigue, time pressure | Identical methodology every time |
| Handling of non-standard bid formats | Experienced reviewers adapt; junior staff struggle | AI normalizes any format automatically |
| Scalability (peak submission periods) | Bottlenecked by available senior staff | Handles any volume simultaneously |
| Audit documentation | Often incomplete due to time pressure | Automatic, complete audit trail |
| Cost per evaluation | High (senior staff hours × days) | Low (software cost, minutes of review time) |
The issue isn't that manual evaluators lack skill — it's that the volume of information overwhelms human attention. A typical GC bid package includes 5-15 bid submissions, each with a pricing schedule, cover letter, qualifications, insurance certificates, references, and technical attachments. Under time pressure, reviewers focus on price tables and skim (or skip) attachments. The result: hidden exclusions and risk clauses go undetected until construction is underway.
AI automates the mechanical work: extracting data from documents, normalizing formats, comparing positions, flagging deviations, and checking document completeness. Humans retain the judgment calls: weighing trade-offs between price and risk, evaluating contractor capability, considering project-specific factors, and making the final award decision. Arctis AI presents complete, structured information so decision-makers can focus on deciding rather than on data assembly.
Teams adopting AI bid evaluation typically start with one project to validate the output against their manual process. Once they confirm the AI catches everything they would (plus items they'd miss), they shift to AI-first evaluation across all packages. Manual review becomes a targeted quality check rather than the primary analysis method. The time savings compound: evaluators spend their expertise on judgment, not data entry.