Systems — Tractionloop
Systems

We build what
your team actually needs.
Not what's on a menu.

Every system we build is scoped to a specific problem inside a specific business. The examples below show the kind of work we do — they're not a fixed catalogue. If you don't see your problem here, that's not a reason to stop reading.

00 — What we build

Any operational problem
with a system-shaped answer.

We're not a tool. We don't have a catalogue you pick from. We're engineers — which means we start with your problem and work backwards to what needs to be built. The scope of what we can build is defined by what you actually need, not by what we've already done.

Two disciplines — GTM Engineering and Recruitment Engineering. Within each, the scope is wide. Below are the outcome areas we consistently work across.

GTM Engineering — outcome areas
Pipeline generation
Getting the right people into your funnel

From ICP definition through to enriched, sequenced, and replied-to. The full top-of-funnel motion — built as infrastructure, not as a campaign.

Outbound emailLinkedIn automationIntent signalsLead scoring
Sales enablement
Giving reps what they need to close

Pre-meeting intelligence. Battlecards. Proposal generation. Call scoring. The systems that let your reps show up better prepared and close faster.

Pre-meeting packsCall gradingProposal generationCompetitive intel
Revenue operations
Keeping the whole engine visible

CRM architecture. Pipeline reporting. Deal analysis. Customer health. The operational layer that connects what's happening in the field to what leadership needs to see.

CRM architectureRevenue dashboardsDeal debriefAccount health
Recruitment Engineering — outcome areas
Candidate sourcing
Finding the right people before the competition does

Automated sourcing pipelines, market signal monitoring, and internal network intelligence. Candidates moving toward you continuously.

Active sourcingMarket monitoringNetwork mappingWarm intro routing
Interview operations
Making every conversation count

Prep that arrives before the interview. Scorecards filled right after. Scheduling that runs itself. The infrastructure that makes every interview feel intentional.

Interviewer prepCandidate briefingScorecard automationScheduling flows
Hiring ops
Running a clean, fast, reliable process

ATS data quality. Offer generation. Onboarding automation. The unglamorous infrastructure that stops good hires slipping through gaps in the process.

ATS data qualityOffer generationOnboarding flowsHiring analytics
How scope is determined
01
Scope call

We map your stack, find what's breaking, and identify where the cost is highest.

02
We identify one system

The single system with the clearest return and most realistic path to live in 14 days.

03
You confirm scope

Architecture designed and agreed before build starts. Day 14 outcome defined upfront.

04
Sprint begins

14 days. Daily updates. Built inside your stack. Live and yours on Day 14.

The examples below are systems we've built. They're here to give you a concrete sense of the work — the problem each one solves, what gets built, and what changes on the other side. They're not a menu you pick from. Every engagement starts with a conversation about your specific situation, and the system that gets built comes out of that.

18 examples Click any card to expand
GTM — 01

Go-to-market examples

9 examples
GTM — 001
Outbound Infrastructure

The foundation most teams skip. Domains configured, warmed, and monitored before any volume runs. Enrichment feeds context into every sequence automatically — personalisation that doesn't rely on someone remembering to do it.

InstantlyClaySmartLeadApollo
The problem

Most outbound fails before the first send. Domains burn. Sequences hit spam. Volume scales before the infrastructure can handle it. This builds the foundation correctly so outbound can grow without breaking.

What gets built
Domain procurement, DNS, and warmup scheduling across multiple inboxes
Enrichment pipeline — verified contact data before any email is sent
Sequence architecture structured by role, industry, and signal
Deliverability monitoring with automated alerts before problems show in open rates
CRM sync — every reply, bounce, and conversion lands in the right pipeline stage
GTM — 002
Pre-Meeting Intelligence Pack

Before a call happens the rep already has what they need — prospect context, a tailored deck from your knowledge base, and talking points matched to the buyer's role. Triggered by the calendar. Delivered to Slack before the meeting.

Clayn8nClaudeHubSpot
The problem

Reps spend the first 20 minutes re-establishing context that already existed. This ensures they walk in already briefed — so discovery goes deeper, faster.

What gets built
Calendar trigger — meeting booked, enrichment starts immediately
Prospect and company research structured: role, context, recent signals
Deck assembled from your knowledge base, matched to industry and buyer type
Talking points flagged by relevance — pulled from real CRM history
Briefing delivered to rep via Slack 60 minutes before the call
GTM — 003
Conversation Quality Layer

Every sales call scored against what good looks like for your team. Coaching summaries routed to managers in Slack. Nobody has to listen to recordings to know what's happening across the floor.

GongClaudeSlackHubSpot
The problem

Sales coaching is reactive — managers review calls after deals are lost. This closes the feedback loop from weeks to hours. Every call scored immediately, patterns surfaced before they become trends.

What gets built
Transcript ingestion from your existing recording tool — no new platform
Scoring model built against your criteria: pain points, objections, next steps
Coaching summary per call routed to manager in Slack within the hour
Weekly pattern report: which reps are improving, which objections go unanswered
CRM notes updated to reflect what was actually discussed
GTM — 004
Account Health Engine

Scoring models that surface expansion signals and churn risk from usage, engagement, and CRM data. CS teams are proactive, not reactive. Accounts flagged before the customer knows they're drifting.

HubSpotn8nLooker StudioSlack
The problem

CS teams find out an account is unhappy when they send a cancellation email. The data to predict it was there — the system to read it wasn't. This changes that.

What gets built
Health scoring model drawing from CRM activity, engagement, and product signals
Risk tiering updated daily: healthy, at risk, urgent
Expansion signal detection: usage spikes, team growth, new use cases surfaced automatically
Slack alert when an account crosses a threshold
Real-time Looker Studio dashboard for CS leadership
GTM — 005
Deal Debrief Automation

When deals close won or lost, reasons are captured automatically — not buried in a CRM note nobody reads. Patterns surfaced monthly. Playbooks updated from real data, not leadership assumptions.

HubSpotClaudeMakeNotion
The problem

Every lost deal contains intelligence — why the champion went cold, which objection wasn't handled, which competitor keeps appearing. That intelligence exists but is never synthesised at scale. This system does it continuously.

What gets built
Trigger on deal close — pulling all notes, calls, and emails into the debrief
Structured output: primary reason, objections raised, competitor mention, deal timeline
Monthly pattern report: top reasons deals are won and lost
Playbook update flag when a new objection pattern emerges
CRM fields populated so the data is queryable, not just readable
GTM — 006
Proposal Generation System

Rep triggers a proposal from the CRM. It arrives in DocuSign within minutes — scoped correctly, on-brand, signed by the right people. No ops involvement. No three-day wait.

HubSpotClaudeDocuSignMake
The problem

A verbal yes loses momentum when the proposal takes three days and three people to produce. The window between commitment and signature is when deals go cold. This system closes it.

What gets built
Rep-triggered workflow from the CRM — one action, no emails to ops
Proposal assembled from deal data: scope, pricing, terms, timeline
On-brand formatting applied automatically every time
DocuSign envelope created and sent with the right signatories
Signed document stored and CRM updated on completion
GTM — 007
Signal-Based Lead Routing

Job changes, funding rounds, tech stack shifts, hiring signals — detected and routed to the right rep with context attached. Your team reaches people at the moment something has changed, not six weeks later.

ClayApollon8nSlack
The problem

Timing is everything in outbound. A buyer who just got promoted, raised, or switched from a competitor is fundamentally more reachable. This system identifies those moments and acts on them automatically.

What gets built
Signal library configured to your ICP: job changes, funding, tech shifts, hiring patterns
Monitoring pipeline running continuously across your target account list
Signal detected → lead enriched → context assembled → routed to rep with recommended action
CRM entry created or updated with signal context attached
Slack notification to the right rep with enough context to act immediately
GTM — 008
Re-Engagement Pipeline

Closed-lost deals from 6–18 months ago systematically revisited. Outreach triggered by time, role change, or company signal — with context from the original deal already loaded.

HubSpotClayInstantlyn8n
The problem

Closed-lost doesn't mean closed forever. Most re-engagement is manual and inconsistent — a rep remembers some deals but not others. This makes it systematic: every deal revisited at the right moment, with the right context.

What gets built
Closed-lost cohort segmented by reason, deal size, and original timing
Re-engagement triggers: time-based (6mo, 12mo) or signal-based (champion moved, company raised)
Personalised outreach built from original deal context — not a generic "checking in"
Positive response routes back to the original rep with full deal history surfaced
Monthly report: re-engagement volume, response rate, deals re-opened
GTM — 009
Revenue Reporting Infrastructure

Pipeline velocity, outbound performance, source attribution, and MRR trends in one dashboard — updated automatically from your CRM and outbound tools. Leadership stops waiting for someone to build the spreadsheet.

Looker StudioHubSpotSheetsn8n
The problem

Revenue reporting is someone's job instead of a system. Numbers are always slightly stale. Leadership asks for data on Friday and gets it on Monday. This makes reporting automatic and trustworthy.

What gets built
Data pipeline from CRM and outbound tools to Looker Studio — no manual exports
Pipeline velocity, stage conversion, source attribution, MRR movement in one view
Automated refresh schedule — current without anyone running a report
Forecasting view: weighted pipeline and projected close rate by cohort
Threshold alerts — issues surfaced before leadership discovers them
Recruiting — 02

Recruitment examples

9 examples
REC — 001
Active Candidate Pipeline

Sourcing runs continuously across every open role — targeted by criteria, enriched before contact, operating across channels. Qualified candidates move into the funnel regardless of what else the recruiter is working on.

ClayHeyreachAshbyApollo
The problem

When a recruiter is deep in three searches, sourcing for the fourth stops. The pipeline runs dry before the last search closes. This system means sourcing never stops — it runs on criteria, not capacity.

What gets built
Role-specific sourcing criteria mapped to enrichment filters: title, seniority, location, tenure
Clay pipeline enriching and scoring candidates before any contact
Heyreach sequences running multi-step LinkedIn outreach — personalised, not templated
Interested candidates routed to recruiter with full context attached
ATS sync — candidates created in Ashby automatically, no manual entry
REC — 002
Hiring Market Monitor

Competitor headcount shifts, candidate availability signals, and market movement — monitored continuously and pushed to Slack when something relevant happens. Context before making moves, not after.

Clayn8nSlackLinkedIn
The problem

A competitor lays off their engineering team on Tuesday. By Thursday other companies have been in candidates' inboxes. Your team finds out Friday. This closes that gap.

What gets built
Monitored list of target companies and roles, configured to your talent market
Signal detection: headcount changes, new openings, layoff announcements
Candidate availability tracking: open-to-work changes in your target profile set
Slack alert on trigger with enough context to act immediately
Weekly market digest: movement patterns and emerging talent pools
REC — 003
Internal Network Intelligence

Your team's connections mapped and cross-referenced against open roles and active candidates. Warm introduction paths surface automatically — because the system found them, not because someone happened to remember.

LinkedInClaySlackn8n
The problem

Every difficult hire exists two degrees away from someone on your team. The introduction never happens because nobody mapped it. The warm path goes unused while a recruiter spends two weeks on cold outreach to reach the same person.

What gets built
Connection mapping across your team's LinkedIn networks — aggregated and searchable
Cross-reference engine: candidates checked against the mapped network for overlaps
Warm intro alert in Slack with relationship context attached
Backchannel surface: who can give a reference or informal read on a candidate
Updated as your team grows and new connections are added
REC — 004
Interview Readiness System

Structured context delivered to interviewers before each conversation. A clear briefing to candidates. Triggered by the calendar — not by a recruiter chasing people down the day before.

Ashbyn8nCal.comSlack
The problem

Interviewers show up not knowing what stage the candidate is at or what they're assessing. Candidates don't know what to expect. Both sides are improvising in a conversation that should feel considered.

What gets built
Interview scheduled → prep workflow triggered automatically
Interviewer brief in Slack: candidate context, focus, scorecard criteria, previous feedback
Candidate briefing email: who they're meeting, what format, what to expect
Reminder sequences for both parties, timed to role and stage
Post-interview scorecard prompt sent within 30 minutes of call ending
REC — 005
Pipeline Data Quality

Automated checks running on schedule — flagging stale records, duplicates, missing fields, and inconsistent stages before they compound into a reporting problem. Clean ATS, every week, without a manual audit.

AshbyMakeSheetsSlack
The problem

ATS data degrades quietly. By the time someone notices, the reporting is unreliable and the audit takes a week. This catches issues on a schedule so they don't accumulate.

What gets built
Daily data quality checks across the ATS
Exception flags: candidates unmoved 14+ days, missing fields, duplicates
Weekly exception report delivered to the recruiter who owns the record
Automated cleanup for low-risk issues where the correction is unambiguous
Data health score tracked over time — quality as a metric, not just a project
REC — 006
Offer to Day One

Verbal yes to signed offer in hours. Signature to Day 1 without dropped handoffs. Document generation, signing flow, onboarding steps, and internal notifications — all automated from the ATS.

AshbyDocuSignMakeNotion
The problem

The gap between verbal yes and signed document is where good hires go quiet. Momentum evaporates while someone tracks down a template and gets approval. This system makes the gap disappear.

What gets built
Verbal offer confirmed in ATS → offer letter generated automatically
DocuSign envelope sent to the right signatories within minutes
Signing triggers next steps: IT provisioning, onboarding checklist, Day 1 schedule
Internal Slack notifications to the right people at each stage
New hire briefing sent automatically: Day 1 logistics, who to ask for, what to expect
REC — 007
Inbound Candidate Scoring

Applications enriched and scored against role criteria before a recruiter touches them. Obvious mismatches filtered. Strong fits surfaced and prioritised. Screening time drops before the first CV is opened.

ClayAshbyClaudeMake
The problem

When a role gets 200 applicants, someone has to sort them. That someone is usually the recruiter, for two hours, doing work a well-configured system handles in seconds.

What gets built
Role-specific scoring criteria defined per search — not a generic template
Application triggers enrichment: LinkedIn, company data, skill verification
Score calculated and written to ATS — candidates ranked before recruiter reviews
Obvious disqualifiers declined automatically with a professional response
Top-tier candidates flagged in Slack for immediate recruiter attention
REC — 008
Hiring Analytics Dashboard

Time-to-fill by role, pipeline conversion, source quality, offer acceptance rate — updated automatically from the ATS. Leadership gets the data without asking for it.

Looker StudioAshbySheetsn8n
The problem

Hiring reporting is someone's job instead of a system. The head of people asks for the time-to-fill breakdown on Thursday and gets it on Monday. This makes reporting automatic and trustworthy.

What gets built
Data pipeline from ATS to Looker Studio — updated on schedule, no manual exports
Time-to-fill by role, team, and hiring manager visible in real time
Pipeline conversion: where candidates drop out and at which stage
Source quality: which channels produce hires that stay vs. hires that don't
Offer acceptance rate tracked and trended — early warning if the process is breaking
REC — 009
Candidate Re-Engagement Pipeline

Strong candidates from past searches — silver medallists, wrong timing, roles that didn't open — systematically revisited when circumstances change. A pool you already built, put to work.

AshbyClayHeyreachn8n
The problem

The second-best candidate from six months ago might be the right hire today. But they're sitting in the ATS under a rejected status because nobody has a system to revisit them.

What gets built
Past candidate pool segmented: silver medallist, wrong timing, role didn't open
Monitoring for candidate signals: new job, recent move, open to work
Re-engagement sequences triggered by signal or time — personalised with prior context
Interested candidates routed to recruiter with full history surfaced
ATS updated with re-engagement status so the pool stays manageable

Don't see your problem here?
Tell us what it is.

These 18 examples are a starting point, not a limit. A scope call is the right place to describe what's actually breaking — and find out what it would take to fix it.

tractionloophq.com — Engineering arm of Tractionloop