Hire faster without hiring a TA team. We build the AI sourcing, CV screening, scheduling, and offer-letter pipelines that take the recurring grind off your recruiters - so the same headcount fills more roles, candidates stop ghosting from scheduling lag, and the panel sees only the people worth meeting.
Indicative timeline. Final scope and dates agreed after the intro call.
Every extra day on time-to-hire is a day your team is short-handed and your best candidates take a competing offer. The build attacks the parts of the process that scale poorly with role count - sourcing, screening, scheduling - so the funnel speeds up without adding recruiters.
Multi-calendar, candidate-driven scheduling with panel rules eliminates the back-and-forth thread that eats a third of every recruiter's week. That time goes back into closing candidates, not chasing calendars.
Seven seconds is not a screen, it is a coin flip. The build does a real first-pass scoring against role-specific criteria - with the rationale and evidence quoted - so recruiters spend their time on the candidates who actually warrant a deeper read.
Automated status updates, candidate-driven scheduling, and an offer pipeline that does not stall on signature-chasing keep the funnel intact through to start date. The candidates you finally got back to do not have a competing offer in hand.
The build treats screening as a high-risk decision: protected attributes are masked, criteria are role-specific and reviewable, every reject has a recorded rationale, and aggregate bias audits ship in the dashboard. Designed to keep your TA lead and your legal team on the same page.
Targeted sourcing on LinkedIn, Gem, Hireflow, or custom scrapers - calibrated to your last twenty good hires, not a generic Boolean. Outreach in your recruiters' voice with your real value-prop, signed by a real person, reply rate measured against your historical baseline.
Role-specific scoring rubrics built from your hire-to-pass calibration. Every score broken into named criteria with the supporting evidence quoted from the CV. Protected attributes masked, recruiter override always available, override rate tracked.
Candidate-driven scheduling across multi-calendar panels, with rules for back-to-backs, panel diversity, and recruiter SLAs. Reschedules and no-shows handled automatically. Time-zone aware. Wired to your ATS so the candidate record updates without human touch.
Structured interview kits per role, distributed to the panel before the interview. Feedback aggregated into a single hire/no-hire view with calibrated scoring, conflicts surfaced, and a debrief deck generated for hiring managers - not a pile of disconnected scorecards.
Offer letters generated from the role and approved comp band, sent for e-signature, and tracked through to start date with automated status updates to the candidate. Counter-offer triage. The dropoff between “verbal yes” and “signed contract” gets visibility for the first time.
One working session with your TA lead and a senior recruiter. We pull the last 12 months of funnel data from your ATS, surface the top dropoff stages, and pick the deliverable with the highest payoff. Scoring rubrics calibrated against twenty real good and bad hires by end of day three.
Usually CV screening or scheduling - the change with the most measurable lift on time-to-hire and the lowest blast radius. Wired to your ATS, scored against your historical baseline, recruiters running real candidates through it by end of week one.
AI sourcing pipeline live with reviewable outreach, structured interview kits in the panel's calendar invites, and offer-letter pipeline wired through to e-signature. Bias audit dashboard turned on.
Expand from pilot pod to the wider TA team, ship the funnel dashboard (time-to-hire, recruiter hours per role, candidate dropoff), and walk your TA ops lead through the runbook. You leave week three with the build live and a baseline you can keep measuring.
Indicative timeline. Multi-region hiring, regulated industries, or unusually large panels can stretch this; we confirm dates after the kickoff session.
Defined scope, agreed in writing before kickoff. No metered hours, no surprise add-ons, no scope creep mid-build. The first week sets the bar - we ship to it, and your recruiters are running real roles through the pipeline by week three.
You own the repo, the prompts, the rubrics, and the ATS configuration. Sourcing tool licences and model usage are billed by the vendor directly to your account - no markup, no reseller margin, no vendor lock-in to us.
Investment is sized to ATS complexity, role count, and compliance scope (EU AI Act, EEOC) after the intro call. We come back with one number, in writing.
Start a projectNo. Every CV score is broken into named criteria - the things you actually care about for the role - with the supporting evidence quoted from the CV. Recruiters see the score, the rationale, and the evidence on every candidate, and can override any decision. We tune the screen against a sample of recent good and bad hires before turning it on, and the dashboard tracks override rate so you can see if it is drifting.
ATS AI is generic - same prompt, same scoring, same outreach across every customer. The build is fitted to your roles: scoring criteria built from your last twenty good hires, outreach in your voice with your real value-prop, scheduling rules that match how your panel actually works. Where the ATS’s built-in flow is good enough, we use it; where it is not, we wrap it with custom logic that lives outside vendor lock-in.
The build treats CV screening as a high-risk decision. No protected attributes (name, age, gender, photo) feed the model; criteria are role-specific and reviewable. Human-in-the-loop is mandatory on every reject. We log every decision with rationale and surface aggregate bias audits (selection rate by demographic where you collect it) so your TA lead can hand the report to legal. Designed to stay compliant with the EU AI Act high-risk requirements.
Your existing ATS. We do not stand up a parallel candidate database. Models receive only the fields needed for the specific decision, and prompts and outputs are logged in your cloud account (AWS, GCP, Azure) - not ours. Candidate data does not train external models.
Three numbers, baselined in week one: time-to-first-recruiter-screen, scheduling lag (offer of an interview to interview booked), and recruiter hours per filled role. The dashboard ships with the build and reports against the baseline so the lift is on the record, not in a slide.
Bot-feeling outreach is the failure mode we are explicitly building against. The build studies your recruiters’ actual sequences, the things candidates have replied to in the past year, and the role’s real value-prop. Outreach is reviewable before send, signed by a real person, and the build measures reply rate against your historical baseline so we know it is working.
Three options: (1) take the repo and run it internally - your TA ops lead owns it, (2) keep us on retainer for new role configs, prompt tuning, and ATS upgrades, (3) scope a follow-on build (offer-letter generation, reference automation, candidate-facing assistants). No pressure to continue.
Tell us where the funnel leaks today - sourcing, screening, scheduling, or offer dropoff. We will come back within one business day with the next step.
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