Industries Sales
Automated workflowsthat help your close.
AI Engineering and software development for sales companies.
Outbound volume stopped working the moment everyone could generate it. What's scarce now is knowing which accounts are genuinely in market, what to say to them, and whether the last twelve touches moved anything at all.
WeGenerateMedia Buying & Call Intelligence
The internal platform a pay-per-call sales operation runs on. Ringba call data ingested every fifteen minutes and reconciled against ad spend in one profit ledger, so every media buyer's own book reads live instead of after the month closes.
- 28%
- Yearly revenue increase
- 15 min
- Ringba call data to live dashboards
- 100K
- Call records per ingestion batch
Why sales teamstrust us with delivery.
A forecast that misses the quarter shows up as lost revenue, not as a bug report. This is why we pay special attention to the most sensitive parts of the system.
The CRM is written by people in a hurry
Stages get updated the night before the pipeline review, next steps say "following up", and half the contacts changed jobs months ago. Nobody is being lazy: keeping the record is a second job bolted onto selling.
The CRM should update itself
Nobody closes faster because a field was made mandatory. An agent reading the call, the thread, and the calendar can move the stage, write the next step, and correct the contacts as the deal moves, leaving the rep to confirm rather than type.
The forecast is a story until it isn't
Commit calls rest on rep sentiment and a stage field someone moved to keep a review calm. The evidence that would predict the quarter sits unasked in your systems: engagement, response latency.
You're selling to a committee
Six to ten people sit on the deal, most never take a meeting, and your champion might change jobs mid-cycle. A system that models one contact and one opportunity is blind to where deals actually die.
Every rep asks the same five questions
How did we price this last time, what do we say about that competitor, who answered this security questionnaire before. The answers sit in recorded calls, Slack threads, and a drive nobody maintains.
What sales demandsfrom engineering and AI.
01Scoring built on live behaviour
Firmographics say who resembles a customer, not who is in market this month. Product usage, hiring and funding events, and how the buying group is engaging right now, scored as they happen, are what separate a ranked list from a list sorted by company size.
02Research finished before the call
The prep a good rep does covers filings, recent news, org changes, and what happened in the last three threads, and it costs twenty minutes before every meeting. Assembled automatically from the same sources, it makes the first conversation sound like the third.
03Capture instead of data entry
Calls, email, and calendar already contain the contacts, the next step, and the competitor that came up, so asking reps to retype them guarantees drift. Writing that back into the system of record automatically pays off everywhere, because every model downstream is capped by how honest the CRM is.
04Territory, capacity, and quota as models
Coverage, patch design, ramp assumptions, and quota setting still run on spreadsheets and last year's numbers in most organisations. Modelled properly, they answer where the next rep should sit and what the plan can actually carry before the year is committed to it.
Real-life stories of triumph.
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Working with Nortik has been nothing short of amazing. They have helped us with our year-long problem of managing our internal operations. Today we're one of the rare firms that has our whole media-buying insights powered by AI.
Nortik’s Impact
Fully autonomous AI Media Buying insighting & forecasting platform built in under a year.
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Frequently asked questions
Everything revenue teams usually want to know before building on their pipeline data. Not here? Ask us directly.
No. We don't sell a bot that sprays sequences. That market is crowded, and buyers have already learned to ignore it.
We build systems inside your stack: scoring on your data, research and briefs assembled from your history, capture that keeps the CRM honest, and automation where a human adds nothing. Where an agent genuinely helps, it runs under policy with a person on the outcomes that matter.
What we do build is the layer underneath. For WeGenerate that was the platform their pay-per-call operation runs on, with call analytics reconciled against ad spend in one ledger.
Yes. Salesforce, HubSpot, Outreach, Salesloft, Gong, and the warehouse underneath them. The value is usually in the seams between those systems rather than in replacing any of them.
We write back into the system of record rather than standing up a parallel one, because a second source of truth is how these projects quietly fail.
Domain and subdomain strategy, warmup, volume pacing, authentication, and monitoring of reputation as an operational metric rather than something checked after a drop-off.
GDPR and CAN-SPAM obligations are enforced in the pipeline, not in a rep's discipline: lawful basis, opt-out honoured everywhere, and suppression that propagates across every tool.
Only if it shows its reasoning. Every score arrives with the signals behind it and what changed since last week, which is the difference between a number reps act on and one they quietly ignore.
It also has to be measured honestly: back-tested against closed-won history, monitored for drift, and compared against a control so nobody has to take the model's word for its own lift.
That's a large part of what we do: AI inside someone else's platform, built multi-tenant from the start, with per-tenant isolation, cost controls, and latency budgets your customers will feel if you get them wrong.
Model spend behaves like COGS in a product like yours, so we design around unit economics: caching, routing, and model choice per workload, decided up front instead of discovered at scale.
A CRM that follows the rep instead of the rep following the CRM. Agents sit on the calls, the threads, and the calendar, then write what happened back into the record: the stage, the next step, the new name who joined the buying group, the competitor that came up.
Reps confirm instead of typing, so the hours go into closing rather than admin. Everything downstream improves for the same reason, because scoring, forecasting, and coaching are only ever as honest as the record they read.