Case Study: Automated Tender Document Validation Agent

An automated system that monitors a shared inbox for RFP/Tender/Bid emails, extracts and validates attached tender documents against a requirements checklist, quality-assures results, and sends a concise, actionable response to the responsible employee.

Project Type

AI & Automation

Core Technologies

n8n, OpenAI LLMs, Outlook (IMAP/SMTP), PDF parsing

Sector

Commercial / Govt.

Impact

85% Reduction in Validation Time

The Business Challenge

Tender submissions arrive as multi-document email packages (bid forms, tax certificates, bank guarantees, licences). Manually opening each PDF, checking signatures and amounts, verifying licence requirements, and writing guidance back to staff is slow and error-prone. Urgent non-compliance can be missed, and response quality varies across reviewers, increasing risk and rework.

The Solution: An AI-Assisted Tender Intake & Quality Assurance

We built an n8n workflow that ingests tender emails, explodes attachments, and extracts key fields from PDFs (e.g., signatures, tax status, bank-guarantee amount, licence presence). It then scores compliance against configurable rules, constructs a strict JSON-only prompt for an LLM (temperature 0.2) to generate a clear, internal summary with next steps, validates/normalises the LLM output, and emails an HTML briefing to the employee for rapid action. The flow enforces reliability through schema-style checks and deterministic prompts before routing.

The Automated Workflow:

1. Email Trigger & Tender Filtering

The workflow listens on the Inbox via IMAP with attachment download enabled, filtering for subjects containing RFP, Tender, Bid etc., and only proceeds when attachments are present.

2. Attachment Explosion & PDF Field Extraction

Each email’s attachments are emitted as individual items. Custom JavaScript parses binary PDFs to pull structured signals (e.g., has_signature, tax_status, amount_gbp, RFP ID, supplier line) using literal scans and regex. A lightweight preview text is kept for debugging. .

3. Grouping, Rules Engine & Scoring

Attachments are grouped by RFP ID and Supplier, then checked against a configurable policy (e.g., BID_FORM_SIGNED, TAX_CERT_ACTIVE, BANK_GUARANTEE_MIN threshold, PEC licence required for Works). The workflow derives pass/fail/na, computes a compliance score (%), and flags mandatoryFail if any mandatory rule is unmet. Then a detailed prompt is constructed to instruct the AI model (GPT-4) to write a professional email.

4. Internal Email Generation & Routing

An HTML briefing is assembled with Summary, Missing/Failed Checks, Risks, and an Ordered Next-Steps list. The subject line prefixes [ACTION REQUIRED] if mandatoryFail is true; otherwise [INFO]. The email is then sent to the designated employee mailbox for immediate follow-up.

Key Outcomes & Business Impact

Accelerated Compliance Review: Automated extraction and rule checks slash time spent opening and scanning PDFs.
Consistent Decisions: Standardised rules and deterministic prompting produce uniform outcomes and guidance.
Actionable Guidance: Clear “what’s missing” plus concrete next steps helps staff resolve issues on first pass.
Reduced Risk: Mandatory failures are flagged prominently; thresholds (e.g., bank guarantee minimum) are verified programmatically.
Configurable & Extensible: RFP policies, required artefacts, and routing targets are easy to adapt for new tenders or jurisdictions.

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