Operating · Denver
AboutCase Studies

Selectedengagements.

What we built, why, and what changed for the business afterward. The engagements below are anonymized, representative composites of the AI systems we design — figures are illustrative, not measured client results. Formal, named case studies will follow as they clear approval.

Representative engagements

Problem, system, result, tools.

Each example below mirrors the kind of work we deliver across AI systems, automation, internal knowledge, and process redesign. The metrics are illustrative and representative — not results from a specific client.

01Professional Services

AI receptionist & missed-call recovery

Problem

A multi-location professional services firm sent every after-hours and overflow call to voicemail. Most callers never left a message — they called the next firm on the list, so new business leaked out the moment the front desk was busy or closed.

System built

A Voice AI receptionist answers around the clock, qualifies the caller, books straight into the scheduling system, and routes anything sensitive to a human. Any unanswered call triggers an automated text-back within seconds so the conversation continues by message.

Result

Roughly 85% of after-hours calls are now answered or returned within two minutes, and around 30 appointments a month that previously died in voicemail are captured. (Illustrative figures.)

Tools integrated

Voice AIScheduling systemTwilio / SMSHubSpot
02B2B Services

Lead qualification workflow connected to HubSpot

Problem

Inbound leads landed in a shared inbox and a form queue. Reps spent the first hours of every day triaging by hand, the best leads sat untouched until someone got to them, and slow first-response was costing deals.

System built

Every inbound lead is scored against the firm's ideal-customer criteria, enriched, and routed to the right rep with a Slack alert and a drafted first-touch reply for a human to approve. Unqualified leads are tagged and nurtured automatically instead of clogging the queue.

Result

First-response time dropped from about six hours to under ten minutes, and reps spend roughly 40% less time on manual triage — time that moved to live conversations. (Illustrative figures.)

Tools integrated

HubSpotSlackWeb formsn8n
03Operations / Field Services

Internal SOP & knowledge assistant

Problem

An operations-heavy business kept its real procedures in people's heads and a sprawl of documents. Staff interrupted senior team members all day with 'how do we handle this' questions, and new hires took months to get up to speed.

System built

A private knowledge assistant trained only on approved SOPs, handbooks, and policy documents answers plain-English questions inside Slack — with citations back to the source so the team can verify every answer.

Result

Around 12 hours a week of senior-staff time is no longer spent answering repeat questions, and new-hire ramp time shortened by roughly three weeks. (Illustrative figures.)

Tools integrated

Private knowledge system (RAG)Google WorkspaceSlackSupabase
04Sales

Proposal assistant for a sales team

Problem

Proposals took days to turn around and depended on a couple of senior people. Quality varied, the backlog grew, and deals cooled while prospects waited.

System built

A proposal assistant drafts a first pass from the deal brief, the CRM record, and approved templates. A salesperson reviews, adjusts, and sends — so the human stays in control of what reaches the client, without starting from a blank page.

Result

Proposal turnaround moved from about three days to same-day, saving roughly six hours of senior time per proposal and clearing the bottleneck. (Illustrative figures.)

Tools integrated

HubSpotGoogle WorkspaceApproved templatesAI agent
05Legal / Compliance

Contract & intake document analysis

Problem

A compliance-sensitive team reviewed every contract and intake form by hand. It was slow, inconsistent across reviewers, and the occasional missed clause carried real risk.

System built

A document-analysis workflow reads incoming contracts and intake forms, extracts and summarizes key terms, flags anything outside policy for human attention, and keeps an audit trail of every AI-assisted step. People make the decisions; the system does the reading.

Result

Initial review time fell by roughly 60%, and reviewers start from a consistent summary with risk terms already surfaced rather than a cold read. (Illustrative figures.)

Tools integrated

Cerebra (private LLM)AirtableSupabaseApproval workflow

Representative examples · figures are illustrative, not actual client results.

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