Teams everywhere are plugging ChatGPT and Claude into their CRM, Slack, and email — expecting the AI to suddenly understand their business.
It doesn't. And the answers it gives you will feel right, which makes it worse.
The root cause
These aren't edge cases. They're architectural constraints — and they explain why "just connect it" fails every time.
LLMs can only hold so much information at once. The more data you feed in, the less reliable the output becomes. Performance degrades long before the context window is technically full.
NoLiMa Benchmark: At 32k tokens, 11 of 12 models dropped below 50% of their short-context accuracy.
When an LLM doesn't have enough information to answer well, it answers anyway. It will pick a confident-sounding response over an honest 'I don't know' — even when the premise of your question is flawed.
Nature Digital Medicine: LLMs complied with illogical requests, prioritizing helpfulness over accuracy — even when they knew the premise was false.
Complex questions require a plan: what data to gather, in what order, and how to synthesize it. Without explicit instructions, LLMs skip the strategy and take the shortest path to an answer.
ICLR 2025, "LLMs Can Plan Only If We Tell Them": Models struggle to recognize whether a goal has been achieved without structured guidance.
In practice
Your CRM is messy. Duplicate contacts, missing notes, outdated fields. When you ask an LLM to find at-risk accounts, here's what it actually does:
What happens under the hood
The NoLiMa benchmark showed that at 32k tokens, 11 of 12 models dropped below 50% accuracy.
The outcome: It grabs whatever is easiest to access — usually surface-level CRM fields or recent notes. It doesn't cross-reference calls, emails, or tickets. It doesn't have a strategy. You get a plausible-sounding answer built on incomplete data.
A different approach
BackEngine doesn't just connect to your tools. We ingest, organize, deduplicate, and permission your data — then give purpose-built agents exactly what they need to answer precisely.
Calls, emails, tickets, Slack threads, CRM records. We pull from the actual conversations — not just the metadata your team remembered to log.
Every conversation is broken into moments, tagged by speaker, topic, customer, and type. Nothing is left as a raw blob of text.
The same issue discussed in an email, a Slack thread, and a call? We recognize it's one signal, not three. Your AI sees the truth, not the noise.
Every user sees only what they should. Permissioning is built into the data layer — not bolted on as an afterthought.
Each question type has a dedicated agent with a clear plan: what data to retrieve, how to reason through it, and when to stop.
Agents access exactly the data they need — and nothing more. No context window stuffing. No shortcuts. Just the right information for the right question.
Connecting an LLM to your tools gives it access. It doesn't give it understanding.
The gap between those two things is where bad decisions live — and where BackEngine was built to operate.