Olli's Judgment: How Enterprise GTM Teams Stop Drifting and Start Winning
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Every enterprise GTM team has a version of this: a handful of reps who consistently close, and a middle of the pack that knows the process but can't replicate the results. The explicit stuff is identical across both groups:
- Same decks
- Same templates
- Same playbooks
- Same training
The gap is tribal knowledge. The accumulated, invisible patterns that tell a rep which stakeholder to thread in when, what a quiet Slack thread actually signals, which plays move the needle versus which ones just seem productive. That knowledge lives in a few heads. When those reps leave, it walks out the door with them.
Olli captures that knowledge, builds it into a dedicated GTM judgment layer, and wraps it around every rep, deal, and forecast review. The few stop being the ceiling. They become the baseline.
Most GTM data looks complete and isn't. Call transcripts live in Gong. Email threads sit in Outlook. CRM state changes are logged in Salesforce... sometimes. The signals that actually predict deal outcomes are scattered, inconsistent, and useless to a machine learning model in that form. That's why most teams that try to "use their data" end up with dashboards that describe the past but can't see the future.
Olli's Unified Data Layer is the unglamorous, hard part that makes everything else work. We pull millions of signals from your entire stack and normalize them into a coherent time-series per deal.
The result is structured data, where the ordering, spacing, and velocity of events are first-class information the model can learn from. A prompt window crammed with context can't do that.
The full ETL pipeline (integrations, normalization, cleaning, structuring) is handled on our side. Your team writes zero lines of glue code. The average customer goes live in under 2.5 hours of IT time.
Capture the invisible signals your stack is missing.
Most sales signals that matter are never formally logged. An AE infers something from a late-Friday procurement email. A champion goes quiet for six days and nobody flags it. A new stakeholder enters a thread and the rep doesn't notice until two weeks later.
Olli ingests from call transcripts, emails, CRM state changes, stakeholder movements, content engagement, and internal Slack threads. No manual logging. One unified layer. If it left a trace in your stack, Olli captures it.
Raw data becomes a training-ready signal.
There's a meaningful difference between data and training data. Dumping your Gong transcripts and HubSpot records into a prompt window gives you context. Normalizing them into a time-series structure per deal gives a model something it can actually learn from. Specifically, the model knows:
- What happened
- In what order
- With what spacing between events
- Against what outcome
Olli structures every signal this way, so the trajectory of a deal becomes legible to machine learning, not only to a human reading notes.
We build your data pipeline, end-to-end.
Getting this data out of a customer's stack (Salesforce, HubSpot, Gong, Chorus, Outreach, Gmail, Outlook, Slack, calendars, CPQ) and normalizing it into a coherent time-series per deal is brutal, unglamorous, and expensive. It's a system we've been hardening across hundreds of enterprise accounts. We do the full pipeline on our side so your RevOps team doesn't build or maintain any of it. Their side is zero-lift. Ours is years of infrastructure you'd otherwise spend 12 to 18 months building yourself.
Think about the difference between a photo and a video.
A photo tells you what's true right now. You can describe it, analyze it, generate something plausible from it. That's what an LLM does with a deal: you hand it a snapshot, it produces a reasonable output for that frame.
A video is thousands of frames in sequence. The meaning lives in the motion between them: what changed, when it changed, what preceded it, what followed. Complex enterprise deals are videos. A champion going quiet in week three of a 90-day cycle means something completely different than going quiet in week three of a 12-month renewal.
An LLM alone can only ever see the photo. To guide a deal you need the video, and to get the video you need ML trained on the motion.
Olli's Private ML Engine is your own model, trained on your wins and losses, bootstrapped by patterns from 150K+ similar enterprise cycles with known outcomes. It compares the trajectory of your current deal against thousands of deals that already ran to closed won/lost, then tells you what's likely to happen next.
Your unique win patterns are your competitive edge.
Every enterprise team closes differently. The stakeholders that matter most, the signals that reliably precede a stall, the plays that move deals in your specific segment: these patterns live in your data, not in a generic benchmark.
Olli's model tracks:
- How your team actually wins
- Where your deals stall, by stage and persona
- Which rep behaviors consistently precede closed-won
Every recommendation Olli makes is tuned to your motion, not averaged across every other team's.
Deal health continuously scored, not periodically reviewed.
By the time a forecast review tells you a deal is in trouble, it's usually been in trouble for three weeks. Olli scores every active deal continuously against outcome-labeled patterns, so you see where a deal is heading before the number moves.
The score is model-driven and updates as new signals arrive, so it reflects the actual trajectory of the deal rather than the rep's last CRM update. Sales managers stop reacting to surprises. They start intervening when there's still time.
Boosted by a global model of 150K+ enterprise cycles.
Your model starts with course knowledge, not from scratch. Olli bootstraps every new customer's ML engine with patterns we've learned across 150K+ enterprise cycles with known outcomes:
- Which plays preceded wins
- Which signals preceded stalls
- Which stakeholder configurations reliably kill deals
As Olli works your cycles, your model learns your specific patterns on top of that foundation. The result: accurate guidance on day one, compounding accuracy with every deal that closes.
A great caddy has played the course hundreds of times. They know the breaks on the green, the hidden bunker on 14, the shots that look safe but will cost you two strokes. That's tribal knowledge: durable course knowledge built from repetition.
A great caddy doesn't stop there. They also read today's conditions: the wind, the humidity, the pin position, how your swing is feeling this morning. They combine durable knowledge with live conditions to tell you what to hit right now, on this specific shot. That's situational judgment.
The moves that actually close enterprise deals, from day one.
Olli sets up your playbook on day one with the highest-leverage enterprise plays, validated across hundreds of cycles with known outcomes:
- Multithreading sequences
- Executive briefs
- Business cases
- Champion enablement
- Competitive positioning
- Negotiation moves
Every play is matched to the stage and persona you're working, including the creative, non-obvious ones that get stuck deals moving. Your reps start from a library of what's actually worked, not a blank page.
The right play, for this deal, at this moment.
The problem with most playbooks is matching. Reps have to figure out which play applies to the deal in front of them. Olli does that matching automatically. Every play adapts to the specific stage, persona, stakeholder mix, and deal trajectory.
A multithreading play for a deal with a strong champion but no exec coverage looks different than one where the champion just went quiet. The model knows the difference and surfaces the version that fits. No rep judgment required to pick the right move.
Every close makes the next deal sharper.
Static playbooks drift. What closed deals 18 months ago stops working as buyers get more sophisticated, your ICP evolves, and your team's motion changes.
Olli's playbook doesn't drift because it isn't static. Every cycle feeds new data back into the model, refining which plays work in which situations, at which stages, for which personas. The plays that consistently precede closed-won get reinforced. The ones that don't get deprioritized. Your playbook becomes a living record of what actually works, not a document that reflects what someone thought would work two years ago.
Execution without judgment is fast guessing: an agent confidently running the wrong play at the wrong moment because it doesn't know any better. Most AI sales tools work the other way. They wait for a rep to remember to check them, prompt them, and interpret the output. By then the moment has usually passed.
Olli flips that. When a trigger fires, Olli pushes the right play automatically, to where the rep is already working, timed to the moment it matters.
The rep's job is to execute. Olli's job is to make sure they're executing the right thing, at the right time, without having to remember to ask.
AI that prompts you, not the other way around.
Most AI tools require the rep to pull. They have to remember to open a tab, run a prompt, and figure out what to do with the output. Olli pushes.
The trigger logic runs on the ML engine, so alerts reflect the trajectory of the deal rather than static rules. When a champion goes quiet past a threshold, Olli surfaces a re-engagement play. When a new exec enters the thread, Olli queues an exec brief. When age-in-stage exceeds the model's expected range, Olli flags it and recommends a specific move.
Delivered where reps already work, not another tab to check.
Adoption is a surface problem, not a training problem. If reps have to leave their workflow to get guidance, most of them won't, especially mid-deal.
Olli delivers plays directly into the tools reps already live in:
- Slack
- Microsoft Teams
- Gmail
- Outlook
- Claude
Guidance shows up where work is happening, at the moment it's relevant. No new interface to learn, no dashboard to log into, no process change to enforce. The rep sees the recommended play in the tool they're already in, and decides whether to run it.
Adapts to how your team actually wins, not how it's supposed to.
Olli watches which plays get run, which get skipped, and which ones consistently precede closed-won. That behavioral signal feeds back into the model alongside deal outcomes, so Olli learns the specific plays that moved the deal at each stage.
Over time, recommendations converge on the actual patterns of your team's best performance. The reps who skip certain plays because they've learned those plays don't work in your market? Olli learns that too. The system gets smarter as the team uses it, which is the opposite of how most sales tools age.
Competitors can copy your playbook. They can clone your prompts. They can buy the same horizontal AI. What they can't replicate is your model, because they don't have your data. Olli creates, protects, and compounds that asset. Every deal makes the next one smarter.
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