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Inbound leads, answered in two minutes — at 3am, on a Sunday.
Form fills and email enquiries wait 6–18 hours for a first reply. By the time a salesperson opens the inbox, 30–50% of those leads have already had a conversation with a competitor.
An AI agent reads each inbound message, drafts a personalised reply that references the lead's company and stated need, books a call slot from the assigned rep's calendar, and logs everything in the CRM. Replies above a complexity threshold are queued for human review.
Median first-response time on a recent logistics engagement dropped from 9h 14m to 4m 22s. Booked-meeting rate up 31% over the following quarter.
- 01Inbound email or web form arrives in your shared inbox or HubSpot.
- 02Agent classifies the lead, enriches with public company data, scores against your ICP.
- 03Personalised draft written, referencing what they actually asked about.
- 04Calendar slot offered from the right rep's availability — no double-booking.
- 05CRM record created, lead routed, draft sent or queued for review based on confidence.
Reps walk in to a pre-sorted queue every morning.
SDRs spend 3–5 hours a day on research and triage — pulling LinkedIn, hunting for company size, finding the right contact, then discarding bad fits. The good leads sit cold in the meantime.
A nightly enrichment + scoring agent pulls company data, checks fit against your ICP rules, drafts a personalised outreach for qualified leads, and routes the rest to a long-cycle nurture list. SDRs pick up the day's queue, already prioritised.
On a 12-rep team, ~62% reduction in research time. Time-to-first-touch dropped from 36h to under 4h. Reps spend their day on conversations, not browser tabs.
- 01Overnight: agent ingests new leads from web, list uploads, CRM imports.
- 02Enrichment from public sources — firmographics, tech stack, recent signals.
- 03Scoring against your written ICP rules — fit, intent, timing, region.
- 04Outreach drafts written for top-tier leads, referencing the actual signal.
- 05Morning: rep opens the queue, prioritised and contextualised.
From discovery call to signed proposal in the same afternoon.
Each proposal takes 2–4 hours to assemble. Salespeople copy-paste from old documents, often with inconsistent pricing, outdated case studies, or sections that no longer match the offer.
A proposal agent reads call transcripts, pulls relevant case studies and pricing from your library, drafts a tailored proposal in your template, and surfaces it for the salesperson to edit and send.
On a recent professional-services engagement: 8–12 hours per week recovered per salesperson. Win rate up because proposals leave the same day, not three days later.
- 01Call recorded, transcribed, and summarised into a structured brief.
- 02CRM data, deal context, and salesperson notes joined into the input.
- 03Case studies matched from library based on industry, problem, deal size.
- 04Proposal drafted in your template — pricing pulled from current rate card.
- 05Salesperson reviews, edits, hits send. Audit trail kept in the CRM.
60% of tickets answered in seconds. Humans handle the 10% that need judgement.
Support handles 200+ tickets a week. Roughly 60% are repeat questions already answered in the help centre. CSAT slips on first-response time. Agents burn out.
A support agent grounded in your documentation, integrated with Zendesk / Intercom / Freshdesk, answers the easy 60% directly, drafts replies for the medium 30%, and escalates the hard 10% to humans with full context. Sources cited on every reply.
40–55% ticket deflection across our portfolio. CSAT up, not down — because the easy stuff gets answered faster and the hard stuff finally gets the attention it needs.
- 01Ticket arrives. Agent classifies intent, retrieves grounded documentation.
- 02High-confidence: direct response with sources cited, ticket auto-closed.
- 03Medium-confidence: draft prepared, human reviews and sends.
- 04Low-confidence or sensitive: escalated to human with summary + history.
- 05Quality loop: every escalation feeds back into retrieval improvements weekly.
"Where's the latest pricing for enterprise?" — answered in three seconds.
Knowledge workers waste 1–2 hours a day searching across Notion, Drive, Slack, and email for information that already exists. New hires take 60+ days to find their feet.
A private assistant connected to your internal sources. Permissions respected per source. Citations on every answer — never "trust me, this is the policy."
5–10 hours per week recovered per knowledge worker. New hires ramp 30–50% faster, measured against role-specific milestones.
- 01Connectors index Notion, Drive, Slack, email, internal wikis — within your tenant.
- 02Per-user permissions enforced; the assistant cannot surface what the user can't read.
- 03Question asked in Slack, browser, or Teams. Answer generated with sources.
- 04Every claim cited with a clickable link to the source document.
- 05Unanswered questions logged for the docs team — turns gaps into a backlog.
90% of invoices process themselves. The other 10% land on the right desk, already flagged.
Finance keys invoices, contracts, and delivery notes into the ERP by hand. Errors leak through. Approvals stall in inboxes. Late-payment penalties add up quietly.
An OCR + extraction pipeline reads incoming PDFs, validates against POs, posts to your accounting system, and routes exceptions to the right person with the discrepancy already flagged in plain language.
On a recent SME engagement: 78% reduction in finance processing time. Late-payment penalties down 92%. Month-end close shortened by four working days.
- 01Invoice arrives by email or upload. PDF parsed, fields extracted.
- 02Validated against PO, contract terms, vendor history, tax rules.
- 03Clean: posted to the accounting system, approval routed by amount + cost centre.
- 04Discrepancy: routed to the right person with the issue stated in one sentence.
- 05Audit trail kept; month-end reconciliation runs against the same data.
Reps walk out of calls with the work already done.
Sales calls end. Notes get written hours later — or never. CRM fields stay empty. Pipeline reviews run on guesswork. Forecasting is fiction.
Automated transcription, AI summary, action items extracted, and the CRM populated automatically — next steps, pain points, decision-makers, deal stage, competitive context.
4–6 hours per week recovered per rep. Pipeline data finally reflects reality, which means forecasting starts to mean something.
- 01Call records via your meeting platform — Zoom, Meet, Teams.
- 02Transcript cleaned, speakers attributed, structure extracted.
- 03Summary written: outcomes, objections, next steps, decision criteria.
- 04CRM fields populated with structured data, not just "call happened."
- 05Follow-up email drafted automatically, ready in the rep's outbox.
Shortlist on day one. Faster offers. Less ghosting.
HR screens 200 CVs for one role over a week. The good candidates wait, lose interest, and accept the offer that arrived first.
A screening agent scores CVs against criteria you define, drafts personalised next-step or rejection emails, and surfaces the top 10 on day one with reasoning attached. Humans approve every send.
Time-to-shortlist dropped from 7–10 days to under 24 hours on a recent recruiting engagement. Offer-acceptance rate up 19% the same quarter.
- 01Application arrives. CV parsed, normalised against the role spec.
- 02Scored on hard criteria (must-haves), soft criteria (nice-to-haves), and red flags.
- 03Top candidates ranked with one-line reasoning and citation to CV evidence.
- 04Personalised emails drafted — next step or polite rejection. Human approves.
- 05All bias-relevant signals (name, photo, age) excluded from scoring inputs.
From blank page to published — at four times the cadence, in your voice.
Marketing posts when there's time. A blog post takes 5–9 days from brief to publish. Brand voice drifts as freelancers rotate. Half the calendar slips quarter-on-quarter.
A pipeline that turns briefs into drafts in your voice, runs them through a tone-and-claims review, attaches citations, and pushes to your CMS for human approval. Cadence becomes a calendar question, not a capacity question.
Recent client (B2B, 18 people in marketing): production time per piece dropped from 11 hours to under 3. Publishing cadence rose from 4 to 17 pieces/month. Lead capture from organic up 38% over six months.
- 01Brief intake — angle, audience, key claims, target keyword, sources.
- 02Draft generated against your style guide and prior published pieces.
- 03Tone, terminology, and forbidden-phrase check before a human ever opens it.
- 04Editor reviews, edits, signs off. Citations linked back to source material.
- 05Publishes to CMS, scheduled to social, internal Slack notice fires.
Campaigns assembled by an operator who never sleeps and never sends a bad list.
Email campaigns get assembled by hand from old templates. Segmentation is "all subscribers" or "list 1 vs list 2." Reporting is screenshots in a Friday slide. Engagement decays quietly.
Behavioural segments rebuilt nightly. Drafts personalised per segment with subject-line and CTA variants. A/B set up automatically. Reports written in plain English with the next move suggested.
On a 32k-subscriber list (D2C client, 18 months in): open rate +34%, click-through +52%, unsubscribe rate −19%. Time spent per campaign dropped from ~12 hrs to under 90 minutes.
- 01Behavioural segmentation rebuilt nightly from CRM, web, and product events.
- 02Campaign brief in; per-segment drafts out, with subject and CTA variants.
- 03A/B harness configured automatically; minimum sample size respected.
- 04Send executed; deliverability monitored, bouncebacks pruned.
- 05Performance report written, with next-action recommendations attached.
New hires productive on day 30, not day 90. Without burning out the manager.
New hires take 60–90 days to ramp. Managers spend 6–10 hours per hire on repeated questions. IT, HR, and access tickets fall through cracks. The same Notion page goes stale, again.
A 30/60/90 plan auto-generated from the role and team. A Slack/Teams concierge that surfaces the right doc, answers policy questions with citations, and tracks completion across IT, HR, and the hiring manager.
On a 140-person manufacturer: ramp time down 45%, manager hours per hire down from 9.4 to 1.6, checklist completion at day 30 rose from 62% to 96%.
- 01Role + team metadata generates a personalised 30/60/90 plan with milestones.
- 02Hire is greeted in Slack; concierge introduces docs, people, and access steps.
- 03Policy and "how do we do X" questions answered from the wiki, with sources.
- 04IT / HR / manager checklists tracked centrally; stuck items chased.
- 05Day-30 / day-60 / day-90 reviews triggered for the manager, pre-populated.
Monday-morning reports that wrote themselves overnight, with the anomalies already explained.
Someone spends 6–10 hours every Monday stitching reports across four tools. By Wednesday they're stale. Variances get noticed late, if at all. The exec team makes decisions on data three weeks old.
Dashboards refresh nightly from your source systems. Anomalies flagged against trend with likely causes inferred from history. A plain-English commentary draft prepared for managers to review and forward.
On a multi-entity SME group: report production down 87%. Variances surfaced 3.2× faster. Quarterly review prep dropped from a 3-day project to a 4-hour edit.
- 01Connectors pull from accounting, CRM, ops, web, and product nightly.
- 02Dashboards refresh — same definitions, same numbers, every time.
- 03Anomaly detection against trend and seasonality; thresholds you set.
- 04Commentary draft written for each section: what changed, likely why.
- 05Manager reviews, edits, forwards. Audit trail of every metric definition kept.
The plumbing between your tools, finally working — monitored, documented, and quiet.
Data lives in six systems. Updating one means manually keying into another. Errors propagate silently. Operators do "system migration" half their day, every day. Nobody trusts any single dashboard.
Reliable, observable integrations between your tools — built on Make, n8n, or direct APIs depending on what makes sense. Failure handling, retry logic, alerts. The plumbing finally just works.
On an operations-heavy 70-person logistics firm: manual handoffs reduced by 41%, job-success rate at 99.7%, errors caught at the source rather than in month-end reconciliation.
- 01Map every handoff — what flows from where, when, in what shape.
- 02Build connectors with retries, idempotency, and dead-letter queues.
- 03Schema validation at every step; alerts when shapes drift unexpectedly.
- 04Observability dashboard — runs, failures, mean latency, error budget.
- 05Documented runbook handed over; we own it on retainer or you do.
Showing 4 of 13 anonymised engagements. Tap to expand the rest.
