What it does
Grow2.ai deploys an AI agent that handles the mechanical part of the procurement process: reading documents, extracting terms, consolidating into a table, and formulating a preliminary conclusion. The manager's role is to review the reasoning and make a decision.
The process looks like this:
- Collecting quotes from file storage. The agent monitors the folder with commercial proposals and pulls in new files — PDF, DOCX, spreadsheets, emails.
- Structured data extraction. From each proposal, price, timelines, warranties, SLA, scope of work, payment terms, and hidden fees are extracted.
- Terms normalization. Different formats and phrasings are brought to a common scale: "delivery within 10 business days" and "2 weeks" are treated as comparable.
- Building a comparison table. Vendors are ranked by criteria in a tabular format with clear highlighting of deviations from the average.
- Summary generation. The agent compiles a short narrative: key differences, what to pay attention to, what risks are visible in the documents.
- Recommendation with rationale. Proposes a candidate and explains the choice — with reference to specific items in the quotes themselves.
The agent uses the Search / RAG Q&A patterns (the manager asks questions about the data: "who has the longest warranty?"), Analysis and insight (data → narrative), and Summarization (long → short).
What automation does not do
- Does not make the final decision for the manager. The choice and responsibility remain with the human — the agent prepares the arguments but does not sign the contract.
- Does not negotiate with vendors. Requests to clarify terms, price negotiation, agreement on contract revisions — manually.
- Does not replace legal and financial due diligence. Verifying the counterparty's reliability, contract review by a lawyer, meetings — still in the human's domain.
Automation addresses a standard bottleneck: dozens of quotes sit in a folder, the manager postpones the review, decision timelines stretch on for weeks. After implementation, the time from quote receipt to a reasoned conclusion is reduced by 70% — that is the stated effect of automation.
How it works
The AI agent is built on a custom stack of several components. At the core is an AI model that reads documents and reasons over extracted data. Around it: a file storage connector, a RAG index for repeated queries, and a prompt template with fixed evaluation criteria.
Technical flow
- Trigger. A new file in the quotes folder, or a scheduled run (once a day or once a week).
- Extract. The agent pulls text from PDF, DOCX, spreadsheets, and emails, normalizes encodings, and removes signatures and service blocks.
- Chunking + embeddings. Documents are split into logical chunks and indexed in a vector store for subsequent RAG queries.
- Field extraction. Values are extracted according to the criteria template: price, delivery timeline, payment terms, warranty, penalties, SLA, scope of work.
- Comparison. Terms are normalized (currency, units, timelines), and a table with deviation highlighting is built.
- Summarization. The agent generates text: key differences, risks, recommendation.
- Output. The report is saved back to file storage and simultaneously sent to the manager via their usual channel.
Implementation steps
- Criteria collection. The operations manager lists the parameters that actually influence the decision. This is the stage where what was never explicitly formulated before first comes out of people's heads and into a document.
- Dataset preparation. 5-10 past evaluations with real decisions are needed — the agent uses them for calibration.
- File storage connector setup. Connection to the current storage (Google Drive, Dropbox, S3, internal SMB share) with read permissions.
- Prompt design. Description of the agent's role, criteria, output format, normalization rules. This is the main part of custom development.
- Testing on historical data. The agent processes past quotes, and the results are compared against real decisions. Discrepancies are a reason to refine the prompt.
- Pilot. Two or three real purchases run in parallel: the manager evaluates the quotes independently and checks the agent's result. Reconciliation.
- Production. The agent works in the main flow, the person handles the final review.
Solution components
Component | Purpose |
|---|---|
File storage connector | Reading quotes from the connected folder |
AI model | Field extraction, comparison, summary |
Vector store | Document indexing for RAG queries |
Prompt template | Criteria, normalization rules, format |
Output handler | Saving the report and sending it to the manager |
The solution type is custom-code. The base logic is assembled for the client's specific process, not configured in a ready-made SaaS. The reason: supplier evaluation criteria differ between a manufacturing company, an IT integrator, and a retailer — a universal template works poorly here. In return, the client gets flexibility: criteria, report format, and exception handling rules are tailored to their process.
The RAG layer provides additional value after the first deployment: the manager asks the quote corpus questions like "show all suppliers with a warranty of more than 2 years" or "where is the shortest payment deferral" — without a separate report, in free form.
Prerequisites
Automation requires a minimum set of conditions on the client side — without them, implementation will slow down.
Data and access
- A structured folder with commercial proposals in file storage (Google Drive, Dropbox, S3, SMB share — connected via a connector).
- 5-10 past procurements with a documented decision for agent calibration.
- Documentation or verbal recording of selection criteria — what exactly we are comparing, which parameters are critical.
- Read permissions on the folder for the service account.
Team readiness
- An operations manager or closure manager who owns the procurement process and is willing to spend 3-5 hours aligning on criteria and the report template.
- A technical contact (CTO, DevOps, or an external contractor) for setting up the connector to file storage.
- Willingness to transfer selection criteria from people's heads into a document. This step takes the most time, because within a company criteria are rarely explicitly formulated.
Timelines
Complexity level week means a median implementation timeline of 6-10 weeks. Of which:
- 1-2 weeks — criteria collection and dataset preparation.
- 2-3 weeks — agent development and configuration.
- 2-3 weeks — testing on historical data and adjustments.
- 1-2 weeks — pilot on real procurements and final configuration.
Pain points
- Review — bottleneck
- Knowledge in heads, not in documents
FAQ
How long does implementation take?
The median timeline is 6-10 weeks. Most of the time is spent not on development, but on aligning selection criteria within the team and calibrating the agent on past purchases. If the company already has criteria documented and 5-10 analyzed cases available, the timeline shifts toward the lower bound. If needed, an accelerated version with reduced functionality and subsequent expansion is available.
What if we don't have a unified quote format?
A mix of formats is the norm in procurement: some suppliers send PDFs, others send emails, others send spreadsheets. The agent is designed exactly for this: it extracts fields from different formats and maps them to a unified scale. The problem is not the mix of formats, but the absence of criteria for comparison. The criteria will need to be defined once during implementation.
What are the risks and what can go wrong?
Three main risks. The first — the agent extracts a field incorrectly when a supplier phrases a condition in an unusual way (for example, a discount applied under a complex rule). The fix — manual review of the first 10-20 reports and prompt refinement. The second — a change in document format from a key supplier. The fix — accuracy monitoring. The third — selection criteria change as the business evolves, and the prompt requires periodic updates.
Is automation suitable for our industry?
The solution is horizontal — it works anywhere commercial proposals from multiple suppliers are regularly compared: manufacturing, retail, IT services, construction, professional services, B2B trade. Industry specifics only affect the comparison criteria and report format — the technical part is the same. For highly specialized procurement (medical equipment, industrial automation, certified materials), more time will be needed for calibration and a larger set of past cases for training.
What to do if selection criteria change over time?
Criteria do change — new suppliers appear, regulations change, business priorities shift. A periodic update mechanism is built into the prompt: once a quarter, an operations manager reviews the criteria together with the Grow2.ai team or independently. The technical part of the prompt is separated from the business part, so an update takes 1-2 hours. A large-scale architectural overhaul is only needed when the procurement model changes fundamentally.
Can the results be integrated into an existing workflow?
Yes. The agent's report is saved back to file storage alongside the original quotes and simultaneously sent to the manager's usual channel — email, Slack, a task in a task tracker. The connector to external systems is configured during implementation. If a workflow engine or another orchestrator is already in use, the agent connects as a step in the existing pipeline. The report structure is fixed, so it is easy to parse further.
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