Case studies

A few recent engagements

Real problems and real results. Client names are withheld and details generalized to protect confidentiality.

80 hrs
saved per month
1 day
proposal turnaround
10 min
to draft a proposal
0
processes stuck in one person's head
80hours a month

80 hours a month back from manual data entry

Four different legacy platforms ran the business, one per department, with no shared data between them. Staff kept customer records in sync by hand — slow, error-prone, and getting worse as the company tried to grow. We integrated the systems around a common customer reference ID, so a record entered once flows everywhere it's needed. The company now saves roughly 80 hours a month at its current size, with savings expected to grow as it scales.

1business day, from a week and a half

From a week and a half to next-day turnaround

Sales opportunities were getting lost in the handoff between the first call and an engineer actually scoping the work — proposals were taking as long as a week and a half from lead to delivery, with no visibility into what was stuck where. We routed inbound requests into a ticketing system with SLA-backed escalation, so nothing sat unanswered for more than an hour, and matched each opportunity to the right person automatically. Proposal turnaround dropped to within one business day of the scoping call, and speed — once the firm's top complaint — became what partners knew them for.

10minutes, not hours

Proposals in 10 minutes instead of hours

A wide, interdependent service catalog meant every proposal needed its own mix of terms, and reps were assembling them by hand — often reusing an old SOW and missing a leftover reference to a different client. We built an AI skill on Microsoft Copilot that pulls from the company's SOW templates, product catalog, and the actual call recordings and emails from that engagement to draft a client-ready proposal, PDF and slide deck included. What used to take several hours now takes about 10 minutes.

0processes stuck in one head

Institutional knowledge that survives a resignation

Like a lot of businesses that size, key processes lived in a handful of people's heads, and things broke down whenever one of them was out or moved on. We interviewed the company's leaders, recorded how their core workflows actually worked, and turned that into a searchable knowledge base — then pointed role-specific AI agents at it. Anyone can now ask a plain-language question and get exact instructions back, with a link to the source document to verify it. Covering for a coworker, or onboarding someone new, no longer depends on who happens to be around that day.

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