OpenAsset Alternative
OpenAsset Alternative for AEC Proposals
OpenAsset added Shred.ai — AI proposal drafting layered on its digital asset library. RFPM structures the staff, project, and resume data the draft is supposed to pull from in the first place.
Why AEC firms look for OpenAsset alternatives
- AI drafting on top of an unstructured library still produces unstructured output. Shred.ai 2.0 drafts proposal content from RFP criteria using OpenAsset's DAM as its source. The draft is only as current as the bios, project tags, and role histories stored there. If the underlying library is messy, the AI inherits the mess.
- Staff qualifications aren't a first-class data model. OpenAsset organizes files and metadata. Staff certifications, role histories, project assignments, and education aren't structured records you can filter against — they live as fields on files, not as a database of people.
- The expensive layer is content quality, not drafting speed. Mid-size AEC firms spend 120-750 hours per year on resume formatting alone. A faster first draft helps, but it doesn't fix bios that haven't been updated since someone's last certification or project sheets that pull the wrong three projects.
- Drafting and retrieval are different problems. Shred.ai accelerates "turn criteria into a draft." The bottleneck most firms describe is upstream: can the team find the right three projects, four resumes, and current certifications in time. Fixing the data layer makes drafting easier. The reverse isn't true.
OpenAsset vs. RFPM at a glance
| Capability | OpenAsset | RFPM |
|---|---|---|
| Primary function | Digital asset management + Shred.ai AI drafting | Structured staff/project data + proposal content generation |
| AI proposal drafting | Shred.ai 2.0 — Proposal Studio + AI Chat with OA Data | Generated from structured records, not free-form drafting |
| Source content structure | Files and metadata in the DAM | Structured profiles, projects, certifications, role histories |
| Staff resume generation | Via Shred.ai drafts from DAM content | Yes — SF330, SOQ, branded formats from structured records |
| Project experience sheets | Via Shred.ai drafts | Yes — generated and exported to DOCX from structured data |
| Project photo management | Yes — full DAM with metadata | Basic — project media within records |
| Staff qualifications database | No (DAM-level metadata only) | Yes — structured profiles with all fields |
| RFP analysis / compliance | Shred.ai — RFP shredding, go/no-go scoring, compliance checks | Ask RFPM Agent — query staff, projects, and RFP context |
| Target firm size | Mid-market to enterprise | 15-200 staff (mid-market) |
| Can use together | Yes — RFPM can be the structured source of staff/project records that Shred.ai then drafts from | |
Key differences
DAM-first AI vs. data-first generation
Shred.ai is layered on top of OpenAsset's digital asset library. The AI is good. The question is what it has to work with. If staff bios in the DAM are last year's version, if projects are tagged by file folder rather than by structured fields, the draft inherits all of it. RFPM treats staff and projects as structured records first — fields, not files — so generation runs on top of a clean data layer instead of an asset library that was never built to be a data model.
The bottleneck most firms describe
When AEC firms describe what kills their proposal week, they don't say "we couldn't draft fast enough." They say "we couldn't get the right three projects, the right four resumes, with current certifications, in time." That's a retrieval and structure problem before it's a drafting problem. RFPM solves it at the source. AI drafting downstream is the easy part once the data layer is clean.
Staff data as the foundation
RFPM treats staff qualifications as structured data — education, certifications, project history, specializations. When a person earns a new certification or finishes a project, you update it once and every future resume, SF330 Section E, and project sheet reflects the change. OpenAsset's DAM stores files and metadata, not a structured database of people — which is why resume data on OpenAsset workflows typically still lives in Word files on a shared drive.
Different procurement decisions
Shred.ai is bundled into the OpenAsset platform. For firms already on OpenAsset, turning on Shred.ai is a small step. For firms that aren't, "buy OpenAsset to get Shred.ai" is a much larger commitment than picking a tool that targets the proposal content layer directly. RFPM is purpose-built for proposal content workflows and does not require migrating an asset library.
Better together than either alone
This isn't a forced choice. A firm standardized on OpenAsset for project photography — and happy with Shred.ai drafts — can still use RFPM as the structured source of staff and project records that Shred.ai pulls from. The DAM keeps doing what it does well. The structured-data layer makes everything downstream sharper.
Who should choose RFPM
- ✓Firms whose proposal week is killed by resume currency, project tagging, and content retrieval — not drafting speed
- ✓Proposal teams that want a structured staff and project database underneath their content, not AI drafting on top of an asset library
- ✓Teams that need to generate SF330 resumes, SOQ resumes, and project experience sheets from one source of truth
- ✓Firms with 15-200 staff who don't want to migrate a full asset library to get proposal AI
- ✓Teams that want Ask RFPM Agent for querying across staff, projects, and RFP context
Who should stay with OpenAsset (and Shred.ai)
- —Firms whose primary pain point is organizing and retrieving project photography and renderings across hundreds or thousands of images
- —Marketing teams that need a full digital asset management system with metadata tagging, permissions, and brand portals
- —Firms already on OpenAsset whose DAM is well-maintained and whose proposal workflow is served by Shred.ai drafts on top of it
Fix the data layer, not just the drafting layer
Mid-size AEC firms spend 120-750 hours per year on resume formatting alone. RFPM turns that into structured records — and resumes, SF330 sections, and project sheets generate from there.
Frequently asked questions
How does RFPM compare to OpenAsset Shred.ai?
Shred.ai drafts from whatever lives in the OpenAsset DAM — staff bios, project notes, files, metadata. The quality of the draft tracks the quality and structure of the DAM. RFPM treats staff and project data as structured records first — certifications, role histories, project assignments — and generates resumes and project sheets directly from those records. The difference shows up most when the underlying content isn't already clean: AI drafting inherits the mess; structured generation forces it to be fixed.
Is RFPM a replacement for OpenAsset?
Not for OpenAsset's digital asset management function — RFPM is not a full DAM and does not replace one. For proposal content workflows, RFPM is an alternative architecture: instead of drafting on top of files and metadata in a DAM, it manages staff qualifications and projects as structured data and generates SF330 resumes, SOQ resumes, and project experience sheets from that source.
Can RFPM manage project photos like OpenAsset?
RFPM includes project media management for photos associated with project records and supports worker profile photos for resumes. It is not a full digital asset management platform. If your primary need is organizing thousands of project photos with metadata tagging, permissions, and brand portals, OpenAsset is the better tool for that specific job.
Do I need both OpenAsset and RFPM?
Depends on the workflow. Firms with a large photo library that also need structured staff and project data for proposals run both — the tools solve different layers. Firms whose main pain is resume currency, project tagging, and content retrieval typically only need RFPM. Firms already on OpenAsset can use RFPM as the structured source of staff and project records that Shred.ai then drafts from.