Signal Graph | Oliv AI
Claude struggles to answeryour quarter's biggest question.
Most AI tools start thinking when you ask a question. They fetch fragments of your data at query time and try to build an answer on the spot. Making them inefficient, Oliv reads every conversation in full and stores the answer to every revenue question before anyone asks it. Watch the difference below. Most AI tools start thinking when you ask a question. They fetch fragments of your data at query time and try to build an answer on the spot. Making them expensive yet slow. Oliv reads every conversation in full and stores the answer to every revenue question before anyone asks it. Watch the difference below.
$0.00$0.00
Finding deals for this quarter 0 found.
Reviewing 30 deals x ~10 meetings x ~100 emails.
Retrieving similarity-matched chunks from vector DB.
Top 12 chunks per query full transcripts truncated.
Tokens processed 0.
Stitching answer from fragments.
Reasoning 0m 00s ...
Slow. Expensive.
Queries raw data from scratch every single time.
Finding deals for this quarter 0 found.
Checking signal graph per deal.
Pain point Compelling Event Timeline MEDDPICC.
Reading 100+ precomputed data points ~0 tokens total.
12 deals missing compelling event or firm timeline.
Ranking on close probability Done.
Output:
8 deals likely to close this quarter.
12 need compelling event or firm timeline.
10 are early stage, start MEDDPICC now.
Precise. Fast.
Pre-computes the signals, so answers are already assembled before you ask.
Cost:
Finding deals for this quarter 0 found.
Reviewing 30 deals x ~10 meetings x ~100 emails.
Retrieving similarity-matched chunks from vector DB.
Top 12 chunks per query full transcripts truncated.
Tokens processed 0.
Stitching answer from fragments.
Reasoning 0m 00s ...
Cost:
Finding deals for this quarter 0 found.
Checking signal graph per deal.
Pain point Compelling Event Timeline MEDDPICC.
Reading 100+ precomputed data points ~0 tokens total.
12 deals missing compelling event or firm timeline.
Ranking on close probability Done.
Output:
8 deals likely to close this quarter.
12 need compelling event or firm timeline.
10 are early stage, start MEDDPICC now.
Slow. Expensive.
Queries raw data from scratch every single time.
Precise. Fast.
Pre-computes the signals, so answers are already assembled before you ask.
The Problem
Retrieval was built for documents. Not for revenue.
RAG works when the answer lives in one paragraph. Revenue answers live across every meeting, every email, every deal — over months. Chunking loses the thread that connects them.
Context gets lost.
RAG retrieves fragments: a few chunks from a few meetings. It can't reason over 10+ calls, dozens of emails, and the full customer lifecycle simultaneously.
Claude hallucinates.
When retrieval returns fragments, a trillion-parameter LLM stitches an answer out of what it thinks fits. It sounds confident. It sounds fluent. It's wrong on the details that matter: champion, competitor, next step, timeline.
Full context is cost-prohibitive.
Every revenue question means re-reading every transcript. At frontier-LLM prices, one question can burn 15 million tokens. The math stops working before you finish rolling out.
THE CORE DIFFERENCE
Synthesis not retrieval, is the way forward.
Most tools retrieve chunks at runtime and ask an LLM to make sense of them. Oliv precomputes the signals per deal, per account, per contact, before you ever open the tab.
Standard Approach:
- Retrieve chunks then guess.
- One general model, fetching similar-looking fragments at query time.
Chunks the conversation into isolated pieces.
Asks the LLM to fill the gaps between them.
Retrieves the few chunks that look similar to the query.
Loses speaker context and the thread across meetings.
Closing Q3 Unverified.
Grounded Fabricated.
Generic LLM fills the gaps with incomplete data.
Inaccurate data makes AI reason falsely & confidently.
Oliv’s approach:
- Read everything and precompute.
- 100+ specialized SLMs, each fine-tuned to answer one revenue question, all reading the full conversation.
- Every meeting, email and note read end-to-end — no chunking.
- Signals precomputed per deal, per account, per contact.
- 100+ SLMs, each fine-tuned to answer one revenue question.
- The LLM receives complete context, not fragments.
How It Works
The insight behind the architecture
Oliv's SLM stack isn't an optimization. It's a fundamentally different way of solving the problem.
Revenue teams often ask the same ~100 questions. Across every B2B company we spoke to, 70–80% of the questions asked about an account were identical. What are the pains? Who are the decision makers? What's the budget? What's the decision criterion? The questions are universal. The context is what's unique.
1/50th size of a frontier LLM.
20B parameters vs 1 trillion. Small enough to be fast and affordable. Focused enough to be accurate.
100+ Revenue-specific SLMs.
Each one trained on a single question — pains, budget, decision criteria, competitive landscape, and more.
< 1% Hallucinations in production.
Narrow scope eliminates generalist drift. The model can't import context it was never trained to consider.
The SLM Library
A model for every revenue question your team asks
Out-of-the-box models across the entire revenue lifecycle, pre-sales and post-sales. Each one is further tuned to your business context.
- Pains & Needs
- Budget Availability
- Decision Timeline
- Decision Criteria
- Success Criteria
- Champion Identification
- Economic Buyer
- Competitive Landscape
- Next Steps
- Risk Signals
Built for your business in under a day
Every company sells differently. Oliv's SLMs don't just run out of the box, they get tuned to exactly what your team needs to track within hours of your POV kickoff.
Oliv's agent studies your website, product pages, and positioning to understand exactly what you sell and who you sell to.
Custom prompts written for your SLMs ensure each SLM surfaces only the insights that matter for your GTM motion.