Enterprise Data AI — Semantic Layer
Solon – Pre-sales AI agent
Proposal Intelligence reads a requirements document and returns a branded Word proposal, an Excel feature list, and an hours estimate grounded in projects we have already delivered. You review and approve each step before anything leaves your screen.
From first read-through to a sendable proposal, consultants spend 12 to 24 hours drafting, looking up past hours, finding services, and formatting. Junior team members cannot help, because the institutional knowledge of services, past projects and pricing lives in a few heads.
Drafting, looking up past hours, finding services, formatting. That is what consultants told us when we asked. One person, every time.
The pre-sales pipeline pulls senior consultants away from the projects they are running. When the queue grows, response times slow, and consistency drops with it.
Drop a requirements document and get four artifacts back. The branded proposal you can send, the estimate behind it, an evaluation report from two independent judges, and an audit log of every decision the pipeline made.
Branded template, fully populated and ready to send.
Feature-by-feature hours breakdown: best, likely, worst.
Two independent AI judges, five dimensions each.
Every decision, sha256-hashed and append-only.
The graph pauses after estimation. Accept the numbers, or redirect to any upstream agent and re-run from there.
Proposal Intelligence is not one model with a complicated prompt. It is a pipeline of small, focused agents, each with a single job. They run in parallel where they can, in sequence where they must, and every step is observable in real time.
Azure Doc Intelligence, with pdfplumber and pypdf as fallbacks. python-docx for Word. XLSX, MD and TXT parsed natively.
Scope check. Reject anything outside the company's portfolio so the pipeline does not hallucinate or burn tokens on it.
Extracts context, requirements, constraints and red flags with severity ratings.
Maps each requirement to a real company's service. No invented offerings.
Hours per feature, calibrated against quality-filtered past runs with a score of at least 0.7.
Graph pauses. Review the estimate. Accept it, or redirect to any upstream agent to re-run.
Structured deliverable list. What we would build, in what order, with dependencies.
Branded .docx in the company's template.
Branded .xlsx with hours and complexity per feature.
Hours realism, overhead completeness, confidence calibration.
5-dimension proposal score. Can hard-fail the run.
agent_id, model, tokens in and out, confidence, trace_id, sha256 output hash. Written to JSONL on every step.
Three models, one routing layer, and a development workflow where every agent was scaffolded the same way. Conventions live in skills and a shared contract file, so seven team members write code that looks like the same person wrote it.
AI pair programmer for every agent, every session, every team member. Same skills, same review pattern, same conventions across the team.
Summary, solution and mediator agents. Fast and cost-efficient where the reasoning surface is small.
Estimation, features, proposal and evaluation. The agents where calibration and judgement matter most.
Drop in one of your real requirements documents. Watch ten agents run end-to-end, approve the estimate, and open the branded .docx and .xlsx your team would send. Make no mistakes, with a paper trail every enterprise client can audit.
By signing up for the waiting list now, you'll secure your spot for early access and claim these valuable benefits.
