sannos.ai
AIGovernance, risk & compliance
Sannos AI
An AI compliance engine used by Big 4 teams to stand up DORA, NIS2, SOC 2 and ISO frameworks in days, not weeks.
Case study · Sales intelligence
Prospects that show up
before you go looking.
A prospecting agent that learns who a rep wants to reach in a five-minute chat, reads billions of third-party intent signals overnight and drops a ranked list of in-market buyers into the inbox every morning.
NextLMUnited StatesWeb app + AI agents
nextlm.ai
Research time
-70%
less time spent building prospect lists per rep
Reply rate
3.1x
higher than the team's previous cold outbound
Onboarding
5 min
from sign-up to a trained, working agent
Signal volume
35B
intent signals processed every single day
Figures reflect pilot customer averages during the first quarter after launch.
The starting point
The challenge
01
Lead lists went cold before anyone called
Static databases showed who fit the profile, not who was actually shopping. Reps burned their best hours dialling people with no reason to talk.
02
Intent data was big, noisy and expensive
Third-party signals arrived by the billion. Without serious filtering, the useful ones were buried under bots, job seekers and competitors.
03
Setup took weeks of ops time
Traditional tools needed ICP workshops, filter trees and a RevOps person to babysit them. Most teams gave up before the first good lead.
04
Nothing learned from the outcome
Whether a lead closed or ghosted, the next day's list looked exactly the same. There was no loop between results and targeting.
What we built
An agent that does the research,
so reps can do the selling
Onboarding by conversation
Reps chat with Stuart, the onboarding agent, for five minutes. He asks the right questions and trains a personal prospecting agent from the answers.
Signal filtering at scale
A multi-stage pipeline scores behavioural, social and firmographic signals and discards noise before a single name reaches the rep.
Morning delivery
Scored and ranked prospects arrive in the inbox or CRM each morning with the context behind every pick. Set it once and it keeps running.
A loop that sharpens
Wins and misses flow back from the CRM, and each agent re-weights its own model so tomorrow's list is better than today's.
How it works
From a five-minute chat to a daily pipeline
Four stages between the first conversation and a booked meeting.
- 01
Rep briefs the agent
Target accounts, roles and buying triggers captured in plain language, no filter trees.
- 02
Signals collected
Billions of daily web, social and company-activity signals ingested and de-duplicated.
- 03
Scored and ranked
Each prospect scored for fit and timing, with the reason attached so reps know the opener.
- 04
Delivered and learned
Leads pushed to inbox or CRM, outcomes fed back to retrain the agent overnight.
nextlm.ai
Stack
LLM OrchestrationGoogle A2A ProtocolVertex AIPythonBigQueryReactCRM Integrations
Results & impact
What changed
after launch
Before
Reps pulled lists from a static database, guessed at timing and spent hours researching every account before a single call.
After
A trained agent delivers in-market buyers each morning with the reason attached. Reps open the inbox and start conversations.
Let's build your next
AI revenue engine.
Tell us what you are building. You will get a clear plan, a fixed estimate and a team that has shipped this kind of product before, usually within 3 hours of the call.
Delivered in this engagement
- Conversational onboarding agent
- Intent signal scoring pipeline
- CRM sync and feedback loop
More work
