Recruitment agencies spend 60-70% of senior consultant time on business development. Not interviewing. Not negotiating offers. Not building relationships with placed candidates. Business development.
And here's the thing: most of that BD is reactive. A job board alert pops up. A referral comes in. Someone remembers to follow up on that conversation from three months ago. Maybe.
The gap is obvious: systematic, repeatable pipeline generation that doesn't rely on a human remembering to follow up. The thesis is simple — what if BD was infrastructure, not improvisation?
That's what we're building at Tiding. And we're doing it in public.
The Stack Architecture
The Tiding BD stack is designed around a simple principle: Zac (the AI) does the research, drafting, and systematic follow-up. Human oversight gates are in place while Zac trains — monitoring quality, reviewing drafts before they go out, handling the conversations that require judgment. Once validated, those gates open. The oversight stays, but the friction goes.
Here's how it flows:
Input Layer
XML Job Feeds • LinkedIn Sales Nav • Trade Directories • Companies House • Manual Research • CRM Imports
Enrichment Layer
Apollo (contacts) • Email Verification • Headcount Data • ICP Scoring • Exclusion Checks • Tiering
Decision Engine (MPC Query)
Candidate Profile → Market Map → Contact Discovery → Exclusion Check → Draft Generation → Queue for Review
Execution Layer
Cold Email • WhatsApp Business • LinkedIn DMs • Follow-up Sequences
Pipeline & CRM Layer
CRM CRM • Discord Alerts • booking system Booking • Dashboard • Reporting • Handoff Protocol
Design principles: Modular (each layer swappable), API-first (everything programmable), Human-in-the-loop (AI drafts, human approves). The stack doesn't replace the recruiter. It removes the administrative overhead that stops recruiters from recruiting.
What Works (So Far)
XML Feed Processing
We processed a single feed containing 1,927 job postings. From that:
The classification happens automatically: Tier 1 (respond within 24h), Tier 2 (48h), Tier 3 (weekly batch). No more scanning job boards manually.
CRM Enrichment
We took a "clean" CRM of 69 companies and discovered it was full of noise. Fireplaces. Fireworks retailers. Hockey clubs. All caught by keyword-matching "fire" without context.
After a reset:
- Deleted 31 irrelevant records
- Enriched 38 remaining with proper headcount, location, and industry classification
- Added 12 major security firms (Chubb, G4S, Mitie, OCS)
- Final mix: 53% fire alarm / 42% security systems / 5% integrated
Data quality beats data quantity. 38 clean, segmented records > 69 messy ones.
MPC Query v2
We rebuilt our Most Placeable Candidate process from scratch. The old version produced 15 empty shell drafts — no contacts, identical subject lines, copy-pasted briefs in every field.
The new version:
- Analyses candidate profile for the VITO hook (Very Important Top Officer appeal)
- Searches market map for matching companies
- Discovers contacts using the right hierarchy (CEO/Director first, not HR)
- Applies location logic (FE colleges need local candidates; training providers can be national)
- Generates personalised subject lines and email copy
- Queues for human review before any send
First run produced 10 complete drafts with contacts, subjects, and email copy. Ready for exclusion check and send.
Infrastructure
- Live 4-brand email infrastructure (Tiding + Sentinel Talent)
- Live Discord webhooks for real-time form notifications
- Live Automated email monitoring across all inboxes
- Live CRM views for companies, contacts, candidates, MPC drafts
The Bolt-On Discovery
Here's something we didn't anticipate: this stack doesn't have to be Tiding-only.
We're currently running a trial with another agency in a market we identified as sub-optimal for Tiding to build a brand in, but where systematic BD would be valuable. Education and apprenticeship recruitment — proven demand, good fees, but not where we want to focus our brand-building energy.
The experiment is testing whether Tiding BD can layer over existing infrastructure:
- They keep their brand, their CRM, their candidate database
- We provide the BD engine: market mapping, contact discovery, MPC drafting, outreach
- Revenue share rather than full ownership
- We're proving the stack is portable
Early signals are promising. First week produced 15 MPC drafts. XML feed integration is live. The stack is proving it can adapt to different schemas and workflows.
This could be a second business line: BD-as-a-Service for agencies that want systematic pipeline generation without building it themselves.
The Challenges (Real Talk)
Data Quality at Scale
The "clean" CRM wasn't clean. The XML feed contained noise. Every data source requires validation and enrichment. Getting data to requisite quality is harder than getting the data in the first place.
Scale vs. Precision
We can process 2,000 jobs in minutes. But identifying which 50 companies are worth targeting? That still requires judgment. We're building heuristics (company size, hiring velocity, role seniority) but the balance between casting wide and targeting tight is still being calibrated.
The "Good Enough" Trap
There's a tension between perfect data and shipped work. We've leaned toward shipped > perfect, but that means living with incomplete records, manual fallbacks when APIs hit limits, and documented workarounds. It's functional but not elegant. Yet.
What's not solved yet:
- Automated exclusion checking (still manual review)
- Response tracking and sentiment analysis
- Dynamic follow-up sequencing based on reply type
- The feedback loop: which outreach converts, which doesn't
Work in Progress
This is infrastructure building. It's unglamorous. It doesn't generate placements directly. But without it, every placement relies on luck and memory.
The current operating cadence:
- Morning: Email triage, pipeline review, heartbeat checks
- Afternoon: Deep work — MPC drafting, enrichment, market research
- Evening: Documentation, learning synthesis, SOP refinement
We're still tweaking to get this perfect. The MPC Query works but needs more test runs. The enrichment layer needs more data sources. The execution layer needs approval workflows, not just drafts.
What's Next
- Complete contact discovery integration for scale
- Run first MPC exclusion check and send batch
- Build response tracking and follow-up automation
- Validate the bolt-on model (30-day trial)
- Document the BD-as-a-Service playbook
The stack is the product. The placements are the proof.
Tiding is an AI-first recruitment company. We build infrastructure for systematic business development, then apply it to markets with persistent skills shortages. This post is part of our build-in-public series — real metrics, real failures, real progress.