Claude struggles to answer your 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.
Finding deals for this quarter0 found
Checking signal graph per deal
Reading 100+ precomputed data points
~0 tokens total12 deals missing compelling event or firm timeline
Ranking on close probabilityDone
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.
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.
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.
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
Conversation data dumped into vector DB
Chunked and indexed
RAG retrieves a few chunks
Small chunks selected by similarity score
Generic LLM fills the gaps with incomplete data
Inacurate data makes AI reason falesly & confidently
Conversation data dumped into vector DB
Chunked and indexed
RAG retrieves a few chunks
Small chunks selected by similarity score
Generic LLM fills the gaps with incomplete data
Inacurate data makes AI reason falesly & confidently
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
Comprehensive data collection and capture process
Oliv captures everything -
Your apps, the web, and your activites
100+ specialized SLMs evaluate fields
One dedicated model for each specific revenue-related question.
Signals precomputed per deal, per account, per contact
Every meeting, email and note read end-to-end, and answers precomputed.
Comprehensive data collection and capture process
Oliv captures everything -
Your apps, the web, and your activites
100+ specialized SLMs evaluate fields
One dedicated model for each specific revenue-related question.
Signals precomputed per deal, per account, per contact
Every meeting, email and note read end-to-end, and answers precomputed.
The insight behind the architecture
Oliv's SLM stack isn't an optimization. It's a fundamentally different way of solving the problem.
The Realization
The Approach
Full Context
The Payoff
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.
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.
Built for your businessin 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.Every company sells differently. Pains relevant to a DevTools vendor are not the same as pains relevant to a RevOps platform. 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.
See how we do itWe conduct in-depth research on your company, ingest your enablement materials, and write tailored prompts so each SLM surfaces only the insights that matter for your GTM motion.
Tailored version of Oliv live within 1 day of POV kickoff
Deep research on your company
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
Each SLM gets a tailored prompt that constrains it to surface only signals relevant to your solution — not every pain a prospect mentions.
Models improve with you
Human feedback and quarterly re-research track new offerings, updated positioning, and new buyer personas as your company evolves.
See Oliv answer your quarter's toughest question.
Book a chat with the Founder to see Oliv in action
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