Problem
The expensive part of tender work is not finding one procurement notice. It is reviewing thousands of noisy opportunities against company-specific eligibility, budget and delivery constraints without missing the few that matter.
All work
AI procurement intelligence
TenderBot: from tender noise to a ranked opportunity pipeline.
A pre-launch SaaS platform that collects procurement opportunities, matches them to a company profile, explains relevance and helps teams inspect the documents behind every decision.
- Role
- Independent product engineer · end-to-end owner
- Status
- Pre-launch · final product hardening
- Ownership
- Product architecture, UX, frontend, backend, data, integrations and launch readiness
- For whom
- Bid teams reviewing procurement opportunities.
- The problem
- Too many notices and documents to inspect manually.
- What I built
- Company-specific ranking, explanations and document analysis in one review workflow.
The workflow behind the product.
What I built
A B2B decision-support product being prepared for launch with teams that repeatedly evaluate public procurement opportunities.
Hard decisions
A bid team needs to understand why an opportunity was ranked and how to correct the model. Profile context, scoring revisions and feedback remain part of the decision trail.
Product surfaces16:9 · open full screen
- 01Procurement sources
- 02Normalized tender cache
- 03Company search profile
System architecture
System map
- 01Procurement sources
- 02Normalized tender cache
- 03Company search profile
- 04Scoring and explanations
- 05Documents and AI analysis
- 06User decision workspace
Current-stage evidence
Internal product telemetry recorded during pre-launch testing. These figures describe the indexed dataset, not customer adoption or commercial results.
The product is in pre-launch hardening. The core workflow and indexed dataset are available for demonstration; final security, billing and operational checks remain explicit launch gates.
Platform breadth
A complete product system, not a collection of screens.
TenderBot combines data acquisition, company-specific intelligence, explainable ranking, document processing and an operational control plane. Every layer supports the same decision trail from source notice to a reviewed opportunity.
Acquisition layer
Multiple procurement sources feed resumable synchronization jobs with explicit source state, retry boundaries and monitoring points.
Normalized tender model
Source-specific notices become one searchable cache that supports deduplication, lifecycle updates and profile-level evaluation.
Company intelligence
Onboarding captures regions, capabilities, constraints, eligibility rules and negative signals as durable decision context.
Explainable ranking
Scoring produces reasons, revision history and feedback paths instead of an opaque relevance number.
Document intelligence
Retrieval, archive extraction, format controls and AI-assisted analysis expose the evidence behind each opportunity.
Operating plane
Authentication, phone verification, usage accounting, administration, health checks and rollback complete the product beyond its interface.
Detailed scope
Inspectable engineering decisions.
What is implemented
- Multi-source tender collection and resumable synchronization
- Normalized tender cache and profile-specific matching
- Search-profile onboarding, rules and negative signals
- Relevance scoring with explanations, revisions and feedback
- Document retrieval, archive extraction and AI-assisted analysis
- Authentication, phone verification and owner-scoped data
- Usage accounting, admin operations and launch health controls
- Controlled release and rollback workflow
Hard decisions
- 01Explainability over a black-box score
A bid team needs to understand why an opportunity was ranked and how to correct the model. Profile context, scoring revisions and feedback remain part of the decision trail.
- 02Durable work outside the request cycle
Large imports and scoring runs are resumable and isolated from interactive traffic, so partial source failures do not erase progress or block the user workspace.
- 03Documents as a controlled boundary
Attachments cross explicit format, size and extraction checks before AI processing. This protects runtime stability, analysis quality and usage cost.
- 04Company context as durable product state
Eligibility rules, negative signals, preferred regions and scoring feedback belong to a reusable company profile rather than a one-off search form. Each review improves the next decision cycle.
Evidence
- Public product preview and authenticated application surface
- Documented daily source synchronization and scoring runs
- Multiple document and archive format adapters
- Internal telemetry for dataset, users and search profiles
- Separate marketing surface and authenticated workspace
- Release identity, service health checks and reversible deployment workflow
The decision path, step by step.
- 01
Collect and normalize source notices.
- 02
Apply the company profile and show the reasons for relevance.
- 03
Inspect supporting documents and analysis.
- 04
Keep the participation decision with the reviewing team.
This walkthrough describes the implemented scope. For an authenticated product demonstration, send a brief; no customer information is exposed here.
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