Your AI Just Made That Up: How Tango Gives LLMs Ground Truth for GovCon

We ran an experiment. We asked an AI a simple question that any business development professional in government contracting might ask:

"Did [Company X] win any task orders on [Vehicle Y]?"

We asked it twice — once without Tango, once with. The difference wasn't subtle.

Without Tango: Confident, Plausible, Wrong

The LLM did what LLMs do. It searched the web, found some press releases and blog posts, and stitched together a reasonable-sounding answer:

[Company X] was selected as one of the awardees on the [Vehicle Y] IDIQ... but I didn't find evidence of them winning any specific task orders.

It found the company's announcement about winning a spot on the vehicle. It found some related work through other contract vehicles. It synthesized all of that into a coherent narrative and concluded — incorrectly — that the company probably hadn't won any task orders yet.

The answer sounded right. It was well-sourced. It even included a hedge: "task order awards aren't always widely publicized, so it's possible there are wins not reflected in public reporting."

That hedge was doing a lot of work, because the answer was wrong.

With Tango: Specific, Sourced, Correct

When we gave the same LLM access to Tango via MCP, the workflow changed completely. Instead of scraping the open web for clues, the agent:

1. Resolved the company to get their UEI (Unique Entity Identifier) — a precise lookup, not a keyword search.
2. Found the vehicle's IDV and identified that it has dozens of task orders across all awardees, with hundreds of millions obligated.
3. Searched for the company's orders under that specific vehicle.
4. Retrieved full details on each order — PIIDs, dollar amounts, dates, NAICS codes, awarding offices.

The answer:

Yes, [Company X] has won task orders on [Vehicle Y]. They hold two delivery orders: one substantive task order worth $5.55M and a $10K initial/administrative order. [Full PIIDs, awarding office, and dates included.]

Contract numbers. Dollar amounts. Dates. Awarding office. NAICS codes. Context about the broader vehicle. All verifiable, all from authoritative federal procurement data.

Why This Matters

This isn't a cherry-picked gotcha. This is the normal failure mode when LLMs try to answer questions about government contracting without structured data access. Without it, you're asking an LLM to reconstruct a database query from blog posts and press releases.

Here's why web search falls short for govcon:

• Task orders aren't press releases. Companies announce IDIQ wins. They rarely announce individual task orders. The federal data systems have them; Google doesn't.
• Procurement data is structured, not narrative. A contract award is a set of fields — PIID, UEI, obligated amount, period of performance, NAICS, PSC. Web search returns prose. What you need is a database query.
• The web is full of stale and partial information. An article from six months ago saying "no task orders yet" stays indexed long after orders are awarded. The LLM can't tell which sources are current.
• Entity resolution is hard. A company name could match dozens of web results. A UEI is unambiguous. Tango's resolve tool maps names to identifiers, which means every subsequent query is precise.

The Structural Problem

LLMs are trained on text. They're very good at reasoning over text. But government contracting data isn't text — it's structured records in FPDS, USASpending, SAM.gov, and a dozen other systems that were never designed for public consumption.

Ground truth in this context means the verifiable, authoritative answer derived directly from structured procurement data — not inferred from web content. When an LLM doesn't have access to ground truth, sometimes it gets lucky. Often it doesn't. And the failure mode is the dangerous kind: confident, coherent, and wrong.

What Tango Does Differently

Tango is a unified API for federal procurement data. We take the messy, inconsistent, poorly documented data scattered across government systems and turn it into clean, structured, queryable endpoints. Contracts, IDVs, entities, opportunities, vehicles, subawards, protests — modeled the way people actually think about procurement.

For AI agents specifically, we built an MCP server (at govcon.dev/mcp) that exposes four tools:

• resolve — Turn names into precise identifiers. "Company X" becomes a UEI, so you stop guessing which entity you're querying.
• search — Query contracts, vehicles, and entities with real filters. This is how the agent found every task order under that vehicle — structured queries, not keyword matching.
• search_opportunities — Find active solicitations, forecasts, and notices from SAM.gov. The opportunities that don't show up in press releases.
• get_details — Drill into any record for the full picture. PIIDs, dollar amounts, dates, NAICS codes — the specifics that turned a wrong answer into a right one.

The MCP server works with Claude, Cursor, the OpenAI Responses API, and any MCP-compatible client. No local install. Connect to the remote endpoint, authenticate with your API key or OAuth token, and your agent has access to the same data that powers the federal procurement system.

The Takeaway

If you're building AI tools for government contracting — or if you're just using AI to do govcon research — the quality of your answers is bounded by the quality of your data access.

Web search gives you narratives. Tango gives you ground truth.

This example is one question about one company on one vehicle. Multiply that across every BD team researching recompetes, every analyst tracking agency spend, every proposal team sizing a market — and the cost of plausible-but-wrong answers adds up fast.

We built Tango so that every AI agent, every tool, and every workflow in this space can start from a foundation of accurate, structured, authoritative data. The free tier is there if you want to try it.

Build something. We'll handle the data.

Learn more at tango.makegov.com. Connect your AI agent via MCP at govcon.dev/mcp. Read the docs.