--- Title: "Trusted, precise answers? Why your AI sounds right but isn’t" Url: "https://devrev.ai/blog/trusted-answers" Published: "2026-05-20" Last Updated: "2026-05-21" Author: "Abhinav Singh" Category: "Blog, Computer" Excerpt: "Computer, by DevRev, gives trusted answers – thanks to Text2SQL, a capability that translates natural language into deterministic SQL queries over your live structured data." Reading Time: 8 --- # Trusted, precise answers? Why your AI sounds right but isn’t ## **The short version: trust matters** Every enterprise AI tool can find documents. Almost none can answer a precise business question – and back it up with the actual data behind it. Computer, by DevRev, solves this – thanks to Text2SQL, a capability that translates natural language into deterministic SQL queries over your live structured data. No hallucination. No stale snapshots. Because Computer’s answers aren’t “fetched” or “retrieved” – they’re computed across your live data. And they’re cited. And permission-aware. The results: Efficiency and knowledge that compounds. Precision and confidence you can stand behind, and act on. Safety built into the architecture – not bolted on as an afterthought. ## **Why this (really) matters** Your VP of Sales asks her company’s shiny-new AI: “Show me my at-risk deals for Q2.” The AI responds. Quickly, confidently: “Great question. You’ve got three deals at risk right now.” Except… there are actually six at-risk deals in the system. That gleaming-new AI missed half of them – because it fetched data through an API at query time… then guessed which fields mattered… then filled the gaps with plausible-sounding fiction. Nobody caught this until the end-of-quarter review. By then, two of those invisible deals had churned. This isn’t a glitch. It’s not a prompt problem – the VP can write. It’s not a model problem. That AI wasn’t cheap. It’s structural, architectural, foundational. And it’s happening in every organization that bolted AI onto fragmented data without solving the precision problem first. We call this the “Hallucination Sink Hole”. And your teams are throwing money into it every single day. ![image](https://cdn.sanity.io/images/umrbtih2/production/9a5cedd164e8180d2b34eb0b3862a3c7d8b2a685-3840x2160.png) **** ## **The Hallucination Hole costs you, big time** Enterprise AI has an extreme accuracy crisis, and too many people are just driving around the hole, pretending it doesn’t exist. WalkMe’s 2026 research confirms it: **54% of employees** bypassed AI tools in the last 30 days and did the work manually. Not because the tools are hard to use – all you need to do is ask, right? Because the answers aren’t trustworthy enough to act on. And when your teams lose confidence, they’ll lose interest. Only **9% of employees** trust AI for business-critical decisions. NINE percent. Enterprises lose **51 workdays** per employee per year to this friction. That’s not a software problem. It’s a business problem hiding inside every AI investment that promised productivity, and delivered confusion. The strangest thing (that’s actually the most obvious thing): smarter models have made the hole bigger. Reasoning over fragmented data produces answers that sound more articulate, more confident – and are still wrong. That eloquence, that seeming-smartness, masks the error. A GPT-3 hallucination was easy to spot. A GPT-4o hallucination reads like a well-written brief from someone who knows your business. Except – they don’t. Retrieval doesn’t solve precision. It just can’t. And most enterprise AI tools today are retrieval systems. They take snapshots of your data, grab what seems relevant at query time, and hope the right information lands in the context window. Ask a retrieval system “How many blocker tickets were created last month across accounts with active renewals?” – and it might return links to documents that mention “blockers”. Or summaries of ticket threads. Or nothing useful at all. A human still has to piece together the actual answer. The AI found something. It didn’t _know_ anything. Again, this is architectural. AI that doesn’t really know your business, that doesn’t understand what’s happening, right now, across all your live data, your living teams, all your breathing systems, is… useless. To get better answers, trusted answers, you need a different architecture. Here are three things that have to be true – no ifs, no buts: 1. **The system has to understand what your data means** – not just where it lives. Entity resolution. Field-level annotation. Relationship mapping across every connected system. The same customer recognised whether they appear in Salesforce, Zendesk, or Slack. 2. **The query is deterministic** – the same question, asked twice, returns the same answer. Every time. No probabilistic drift. No “Well, it depends on how the model interprets it today.” 3. **Permissions have to be enforced before the model sees anything** – the AI knows who’s asking and what they’re allowed to see. Not after the fact. Before. Most AI tools satisfy zero of these three requirements. Some manage one. Almost none manage all three. But Computer manages all three. By design, not by configuration. ![image](https://cdn.sanity.io/images/umrbtih2/production/9315c7c610ca868f8309beddc1883a03fcb27ee1-3840x2160.png) ## **How Computer delivers precision, trusted answers** Computer doesn’t fetch your data at query time. Thanks to Computer AirSync, our patented two-way sync engine, all your data is organized, curated, and most importantly: understood. All that data lives in Computer Memory – our also-patented knowledge graph. This is a living system, a digital twin of your company, with entity resolution across every connected system. Permissions are inherited from your source systems, and enforced at the row and field level. This is why we say that Computer is the only AI with native “Shared Memory”. And it’s what makes precise, trusted answers possible. When you ask a question, Computer’s Text2SQL engine translates your natural language into a precise SQL query against this live knowledge graph: - **The question:** “Which enterprise accounts in APAC with open critical bugs renew this quarter?” - **What retrieval does (badly):** Returns links to documents that _might_ contain pieces of the answer. A human still has to work, and double-check. - **What Computer does (very well):** Translates the question into SQL. Which means it can then join the account table with ticket data, renewal dates, severity levels, and region tags. It then answers with the exact list – with every record traceable, every source cited. The actual answer, calculated from actual data. Three retrieval layers work together to make this happen: 1. **Text2SQL** – converts natural language to SQL over structured data. Returns exact facts (like “revenue is $2.4M”) not document summaries. 100% deterministic. 2. **Vector search** – handles unstructured content through semantic matching. Retrieves only the most relevant passages – not entire documents. 3. **Reverse index** provides lightning-fast exact lookups – ticket IDs, account names, error codes – without scanning thousands of records. **** ## **Intelligence that compounds. With fewer tokens used.** Here’s where it gets even better: because Computer’s Shared Memory delivers precisely curated context, rather than forcing the AI to search broadly, answers are more accurate while using fewer tokens. Better results, at lower cost – that’s an architectural consequence, not a feature checkbox. And here’s where it gets… even better: thanks to AirSync, and Computer Memory, and that “living digital twin” thing, Computer’s Shared Memory gets smarter with every answer, every interaction. It compounds, like interest. Week 1: it’s really, truly great. A few months in – your teams will never (ever) look back. ![image](https://cdn.sanity.io/images/umrbtih2/production/24bd38112be256939a44f6999c7be519e210c7e7-3840x2160.png) **** ## **Why safety is foundational, not optional** Here’s something that doesn’t get discussed enough: most AI systems have no concept of _who_ is asking the question. When data is fetched at query time through APIs, there’s no permission awareness. No understanding of role boundaries. The model sees everything – or guesses what to include. Computer flips this. Every query runs _based on_ the individual user’s permissions. Every answer respects the same access controls your source systems already enforce. If you can’t see a record in Salesforce (or your boss’s salary) because you don’t have permission to – Computer won’t surface it. _Can’t_ surface it. For any enterprise with compliance requirements, governance concerns, or regulated data – that distinction is everything. ## **Get out of that hole, with better answers** Across customers using Computer’s precise, trusted answers today are making a real, measurable difference. - **8–25% increase** in employee productivity. - **12 hours** saved per employee per month on average. - Ticket resolution up to 75**% faster**. But the number that matters most? Trust. Teams stop bypassing the AI. They stop fact-checking every response manually. They ask, get a precise answer, and act with confidence. The model is the commodity. Trust and precision are the real advantages. Text2SQL. Entity resolution. Permission-aware, real-time data composition. Deterministic queries over live systems. Your team shouldn’t have to fact-check, double-check, then triple-check their AI. They should ask a question, and trust the answer – completely, immediately, safely. That’s what we built. And it’s available right now. [See how Computer works →](https://devrev.ai/how-computer-works) [Book a demo →](https://devrev.ai/request-a-demo)