From document ingestion through post-generation editing — one integrated workflow. Grouped below by the phase of your work.
Load your company's institutional knowledge — every past proposal, brochure, safety manual, personnel resume, past-project brief — and keep it fresh as it grows.
PDF, DOCX, PPTX, images, TXT. Bulk folder ingest with per-file progress and cancel-anytime behavior.
Extracts diagrams, org charts, Gantt visuals, and process-flow drawings into structured JSON — not just alt text you can't search.
Updated documents pick up changes automatically. No need to rebuild the whole knowledge base when you replace one file.
See exactly which chunks got created, what's in them, and how they score for a given query. Debug retrieval without guesswork.
Startup scan catches corrupted or truncated embeddings before they poison retrieval. Recovery dialog lets you re-embed affected files in place.
Renders slides to PDF before vision processing, so a 40-slide deck fires 40 vision calls, not 400 per-image ones.
Load an RFP, click one button, get a structured breakdown of everything you need to respond to — with confidence scores.
Pulls scope-of-work activities, deliverables, milestones, key personnel, evaluation criteria, and compliance requirements into editable tables.
Each requirement graded Fully / Partially / Not Addressed / Invalid against your draft proposal. Auto-generates a compliance matrix DOCX.
Every extracted item carries a confidence tier — HIGH, MEDIUM, or LOW — so you know what to trust and what to review.
Consolidated tables per section, ready to paste into your proposal or share internally with the estimator or project manager.
Every cell hand-editable. Changes propagate to downstream draft generation, so a correction to the scope table lands in the proposal.
Windowed-analysis findings get routed to the right category, and residual findings cluster into "Findings" groups so nothing gets orphaned.
Ask questions across your knowledge base or an individual RFP. Answers cite their sources so you can verify what's real.
Natural-language questions against your entire company knowledge base with source citations and per-answer confidence tier.
Same UX but scoped to the current opportunity's RFP documents. Ask "what's the deadline" or "what deliverables are mandatory" and get direct answers.
Follow-up questions reference the prior turn. Say "make it more concise" or "add an example for each" and the model understands.
Every claim tagged with source file plus chunk id, so you can jump to the underlying text to verify.
Long answer heading in the wrong direction? Cancel and re-ask. Clean shutdown, no orphan threads.
One click generates the full proposal: outline, each section, cover letter, executive summary, conclusion, and compliance matrix.
Outline → per-section writer → cover letter → executive summary → conclusion → compliance matrix. All from your analysis grid + knowledge base.
Each section reviewed against the RFP's requirements before final polish. Catches gaps before they land in the customer's hands.
Cover letter, exec summary, and conclusion each pass through refinement cycles until they hit a quality bar.
Auto-generated 3-tier grid tying each RFP requirement to its response location in your proposal.
36 named styles across Analysis, Proposal, and Cover Letter families. Edit once in Word — every future export inherits your house style.
Regenerate the whole proposal or just one section you weren't happy with.
The first draft is never the last draft. AI Assist lets you polish and revise sections in place, with the LLM on tap.
Highlight text, ask the AI to rewrite, expand, condense, soften, or strengthen. Preview before accepting.
The model matches your document's existing tone and voice, not its own defaults. Consistency across sections.
AI Assist can only reference facts already grounded in your KB and the RFP. It won't fabricate a client name or invent a past project.
Edit in the app or edit in Word. Both directions are preserved on the next regeneration.
Built for regulated industries that can't send client data to a SaaS vendor. Air-gap capable, local LLM support, zero telemetry by default.
Runs with zero external network access. Cloud LLM support is opt-in per session, not default.
Qwen2.5, Llama, Mistral, and others via llama.cpp. Full on-device inference on any 24 GB+ GPU.
Mix Local for sensitive extraction with Gemini for cheaper drafting — or lock everything to Local. Per-consumer, not per-app.
Model choices, prompts, and thresholds ship as an encrypted license artifact. No plaintext credentials on disk.
No phone-home. Support-form uploads are opt-in per submission with visible redaction of what's being sent.
API keys, license keys, and session tokens are auto-scrubbed before any outbound log ships to us for support.
All the boring parts that make an AI product actually reliable for daily use — not just a demo.
Automatically computes the safe context window for your model given your available VRAM. Warns before out-of-memory crashes take down a run.
llama-server lifecycle handled automatically. Safe eject on cancel, orphan sweep on startup, no zombie processes eating VRAM.
Cloud backends discover their tier ceiling from 429 responses, pace calls automatically, retry cleanly. TPM budget tracked per provider.
When an LLM output gets cut off, Map-Reduce splits the input and requeues automatically. Retry-with-bumped-budget on single-shot truncation. No silent output loss.
WHEA / TDR / BSOD event scan on launch. Surfaces hardware issues before they crash a run — with a full Hardware Dashboard on demand.
Close-time review of every non-cosmetic error from the session, with a link to full logs. Nothing hides.
Windows-desktop first. IT can deploy, users can activate offline, and vendor updates ship as encrypted config — not code redistributes.
MSI-style installer via Inno Setup. IT can deploy per-machine or per-user. No admin required for user-scope install.
One copy at a time on a given machine. No double-launch chaos over shared llama-server ports or GPU allocations.
Activate once online, or supply an activation code manually. No phone-home license check on every launch.
Activate the seat once, done. No dependency on Okta, Azure AD, or any identity provider.
Vendor ships new prompts, models, or defaults as an encrypted config file. Users load via Settings — no re-install, no re-compile.
Everything the app does is logged locally to a rotating session log you own. Not shipped anywhere unless you attach it to a support form.
Video demos of each phase are on the Demos page, or request a beta invite to try it against your own RFPs.