AI for Government Tenders: How ElevoraX Is Using Artificial Intelligence to Win on the GeM Portal and Evaluate Counter-Prices in 2026

India's Government e-Marketplace — GeM — has become one of the most consequential procurement platforms in the world. With over 65 lakh product and service listings, more than 73,000 buyer organisations, and cumulative orders crossing Rs. 4 lakh crore in FY2025-26, GeM is the primary channel through which the Government of India and its agencies procure everything from office stationery to enterprise software to construction services. For the businesses selling on GeM — from small manufacturers in Tier-3 cities to large system integrators — understanding how to price competitively, respond to counter-price requests, and evaluate tenders accurately has become a core commercial capability.

ElevoraX has been working with GeM sellers and government tender participants across Uttar Pradesh, Madhya Pradesh, and Rajasthan for the past eighteen months, applying AI to the pricing, evaluation, and documentation challenges that determine whether a tender bid wins or loses. This article describes what that work looks like, what AI can and cannot do in the government procurement context, and how ElevoraX can help your organisation compete more effectively on GeM and in the broader government tender ecosystem.

The GeM Pricing Problem

Pricing on the Government e-Marketplace is deceptively complex. The naive approach — list at the lowest price in the category — is a trap. GeM's buyer algorithms, the Dynamic Pricing feature, and the Counter Price mechanism mean that the listed price is often not the winning price. A seller who lists too low destroys margin and sets a precedent for future tenders. A seller who lists too high never surfaces in buyer searches or gets eliminated in the first round of comparative evaluation. The optimal listed price is a function of category competitiveness, buyer behaviour patterns, seasonal procurement cycles, the seller's quality certification status, and the specific buying organisation's historical counter-price behaviour — none of which a human analyst can process accurately at scale.

Counter-price requests — where a government buyer requests a lower price than the listed rate before committing to an order — add another layer of complexity. Should you accept the counter-price? If not, what counter-counter-price should you propose? The answer depends on the order volume, the buyer organisation's historical payment timelines, the competitive set of sellers who could fulfil the same requirement, the cost structure of the specific SKU, and whether the relationship with this buying organisation has long-term strategic value. Making these decisions correctly across dozens of simultaneous GeM transactions is beyond human cognitive capacity without decision support.

How AI Changes the GeM Pricing Equation

Market Price Intelligence at Category Level

The first application of AI in our GeM work is building a continuously updated picture of pricing across a seller's category. Using automated collection pipelines — the same infrastructure described in our market research article — we monitor listed prices, recent order prices (which are publicly available on GeM for transparency), and new entrant pricing across the relevant product categories. This data is fed into a price intelligence model that identifies the current price band in which orders are being placed, the floor price below which sellers are operating at unsustainable margins, and the ceiling price above which a listing becomes non-competitive. The seller's listed price is then positioned within this band based on their specific cost structure and strategic priorities.

Counter-Price Evaluation and Response Optimisation

When a counter-price request arrives — and on active GeM catalogues, multiple requests can arrive simultaneously from different buying organisations — our AI system evaluates each one individually. The evaluation considers the order quantity and its margin contribution at the counter-price, the historical behaviour of the specific buying organisation (does this buyer always negotiate, or is this a one-time request?), the competitive alternatives available to the buyer at the requested price point, and the seller's current inventory and capacity position. The output is a recommendation — accept, decline, or counter — with a specific suggested counter-price and a confidence score. Human decision-makers retain final authority, but the decision support reduces response time from hours to minutes and improves the quality of decisions made under time pressure.

Tender Document Analysis and Scope Extraction

Beyond GeM's direct procurement, ElevoraX works with clients participating in traditional government tenders on the CPPP portal, state government e-tendering portals, and department-specific procurement systems. These tenders involve complex bid documents — often hundreds of pages of technical specifications, eligibility criteria, terms and conditions, and evaluation matrices — that must be analysed accurately to determine whether a bid is viable and what price is competitive. AI-powered document analysis can extract the technical scope, mandatory qualifications, evaluation criteria and their weightings, past order values for similar tenders from the same department, and market rates for the required goods and services from a tender document in minutes rather than the hours or days it takes a human analyst.

The output of this analysis feeds a bid viability assessment — does this organisation meet the eligibility criteria? — and a price estimation model that suggests a competitive bid price based on the scope extracted and the historical award prices for comparable tenders. For organisations that participate in ten, twenty, or fifty tenders simultaneously across multiple departments and states, this automation is not a convenience — it is the only way to compete across that volume without either missing viable opportunities or submitting under-analysed bids.

The GeM AI System We Built: Architecture and Components

Data Collection Layer

GeM's public data — order histories, price lists, buyer organisations, and tender notices — is the foundation of the price intelligence system. Our collection layer uses the GeM APIs where available and structured collection for publicly accessible data where APIs are not available, processing and normalising this data into a clean analytical dataset updated daily. This dataset covers over 200 product categories and 18 months of historical order data, giving the pricing models sufficient signal to identify seasonal patterns, volume-price relationships, and buyer-specific behaviour.

Price Estimation Model

The price estimation model is a gradient-boosted regression model trained on historical GeM order data, with features including product category, technical specifications, seller certification level (OEM, authorised reseller, etc.), buyer organisation type, order quantity, seasonality, and the current competitive price distribution. The model outputs a predicted award price range with confidence intervals — the range within which, based on historical patterns, a bid at a given price is likely to be competitive. For new categories where historical data is limited, the model falls back to comparable-category inference with wider confidence intervals.

Document Intelligence Layer

Tender documents — in PDF, Word, and increasingly in structured XML formats from newer government portals — are processed through a document intelligence pipeline. An LLM with retrieval-augmented generation (RAG) indexes each document and answers a standardised set of extraction questions: What is the technical scope? What are the eligibility criteria? What is the evaluation matrix? What are the penalty and performance guarantee provisions? What is the estimated project value? The structured answers are verified against the source document by a second-pass validation model before being used in bid preparation.

Bid Preparation Assistance

Beyond price estimation, the AI system assists with bid document preparation — generating compliant technical responses to standard tender sections, populating eligibility documentation checklists, flagging missing documents before submission, and formatting financial bids to match the specific format requirements of each portal. This preparation assistance is where a significant proportion of preventable bid rejections occur: technically viable, competitively priced bids that are rejected at the first stage because a supporting document was missing or formatted incorrectly. The system maintains a document library for each client and automatically maps available documents to tender requirements, surfacing gaps in time for remediation.

What AI Cannot Do in Government Tendering

It is important to be specific about what this system does not do. AI cannot — and ElevoraX would not build systems that attempt to — manipulate the GeM platform, interfere with other bidders, access non-public procurement data, or influence evaluation decisions. The pricing and bid preparation AI we build operates entirely on publicly available data, assists human decision-makers rather than replacing them, and is designed to improve the quality and competitiveness of legitimate bids. Government procurement rules exist for good reasons, and the responsible application of AI in this domain means working with those rules, not around them.

Results Across Our GeM Clients

Across the GeM sellers and tender participants we have worked with over the past eighteen months, the consistent outcomes have been: a reduction in bid preparation time of sixty to seventy percent for organisations participating in multiple simultaneous tenders; an improvement in bid success rate of fifteen to twenty-five percentage points compared to the pre-AI baseline; a reduction in counter-price acceptance decisions that were later identified as unprofitable from approximately forty percent of accepted counter-prices to under fifteen percent; and an expansion in the number of tenders a team of given size can actively participate in, typically by a factor of three to four.

“ElevoraX is actively expanding its Government Tender AI practice across Uttar Pradesh, Madhya Pradesh, Rajasthan, and Bihar. If you are a GeM seller, a PSU procurement team, or a private organisation that regularly participates in government tenders and wants to explore how AI can improve your competitiveness, contact us at [email protected] or reach out via WhatsApp. We offer an initial assessment of your current tender participation and a specific proposal for how AI can improve your outcomes.”

The Broader Opportunity: AI in Indian Public Procurement

Government procurement in India is undergoing a structural digitalisation. GeM is the most visible manifestation, but the same transformation is happening across state government portals, public sector unit procurement systems, and defence procurement channels. Each of these represents a data-rich environment where AI can improve decision quality for both buyers and sellers. For buyers, AI can improve procurement planning, detect anomalous pricing, and flag potential quality risks. For sellers, it can level the playing field between large organisations with dedicated bid teams and smaller organisations with equivalent technical capability but less bid management resource. ElevoraX's work in this space is the beginning of a much larger capability we are building around AI-assisted participation in the public sector economy.