Scout.
Manual sourcing is slow, inconsistent, and impossible to run at volume. Scout is a five-phase AI pipeline that monitors multiple sources, scores each opportunity against a target profile, personalises outreach, and tracks every response, twice a day, untouched.
The problem
Outbound sourcing at volume is broken.
Sourcing across channels by hand takes hours, and most of that time is spent on leads that are obviously wrong. The few genuinely relevant targets get the same unfocused attention as everything else. Follow-up is inconsistent. Nothing is tracked.
The real problem isn’t finding opportunities, it’s signal-to-noise. Hundreds of signals exist; a handful are worth pursuing. Finding them manually at volume is not a sustainable operation.
Scout solves it with an AI scoring layer that reads the same signals a human analyst would, against a defined profile, without the time cost.
Before Scout
After Scout
How it works
Five phases. One pipeline.
- 1
Data Ingestion
Scrapes multiple sources on a schedule, normalises every listing into one schema, and deduplicates against previous runs. Nothing missed, no duplicates through.
- 2
AI Scoring Against ICP
Claude Haiku scores each opportunity against a defined Ideal Client Profile and returns a fit score with reasoning. Chosen for cost and latency, hundreds scored for pennies.
- 3
Profile-Matched Personalisation
For anything above threshold, the system writes outreach tailored to that specific target and context. Not a template, it reads the full brief and writes to it.
- 4
Outreach Automation
A Telegram flow presents scored cards with pre-drafted outreach for one-tap approval. Daily volume caps protect sender reputation; GDPR cleanup is built into the schema.
- 5
Analytics & Follow-Up
Every contact is tracked, opens, responses, follow-up timing, so scoring thresholds and outreach copy keep improving over time.
The only human touchpoint
A Telegram approval flow. Each morning, scored cards arrive with pre-drafted outreach. One tap approves and sends; one tap archives. A deliberate UX call, make the daily review take under five minutes, not replicate a full CRM.
Model choice
Claude Haiku over GPT-4
Lower cost and faster latency for a task that runs at volume. Scoring needs consistency and speed, not frontier reasoning.
Architecture
Volume caps built in
Daily send limits set at the schema level, not bolted on later. Protecting sender reputation was a day-one requirement.
Compliance
GDPR from day one
Contact cleanup is baked into the data schema from the first version, not retrofitted at the end.
Memory
Living spec file
A continuously updated implementation record prevented feature regression across all five phases.
Technology
Built with the right tools.
Every tool was chosen for a reason. Claude Haiku scores at volume without burning budget. FastAPI serves the internal endpoints. Telegram delivers approvals without a separate dashboard. SQL keeps the data model simple and queryable.
Each pipeline stage is discrete. If one fails, the others continue. Failures are logged and surfaced, never silently dropped.
Why no vector database?
Scout uses structured AI scoring, not semantic search. A prompted score with reasoning is more interpretable and auditable than a similarity vector. You can read why a lead scored 8/10. You can’t read a cosine distance.
What we learned
The things that only show up in production.
Model latency matters more than capability at volume. GPT-4 scored marginally better; Haiku scored good-enough at a fraction of the cost and 4× the speed. For a task running hundreds of times a day, the trade-off is obvious.
The approval UX is as important as the pipeline. An early version sent raw scored data to review. Nobody read it. One-tap Telegram job cards cut review time from 20 minutes to under 5.
GDPR compliance is not a feature you add at the end. Building contact cleanup into the schema from the start cost nothing. Retrofitting it would have taken days and added risk.
A living spec prevents drift across long builds. A five-phase project built over months regresses without a continuously updated record of what was decided and why.
Need a pipeline built for your process?
Scout was built for outbound. The same architecture applies to any repetitive sourcing, scoring, and outreach workflow. Tell us the problem.