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Case Studies

Proof, shown as a trajectory.

Every build sits on a knowledge layer we keep true and stay accountable for. The numbers below are the proof: they climbed because we kept the layer correct over time. All identities are anonymized — real figures and names are shared on a discovery call.

A proven pattern

The support-agent pattern, proven across three industries.

The same method — knowledge layer, in-conversation answers, human escalation only when the agent genuinely doesn't know — repeated across three very different businesses. Not four one-offs; one repeatable approach.

Customer Support Chat Agent

50% fewer tickets escalated to a human80% fewer tickets escalated to a human

Problem. A growing support inbox where ~80% of tickets were repeat questions answered in product docs no customer reads.

Approach. Built a knowledge layer from product documentation, historical tickets, and escalation rules. The agent answers from the layer in-conversation; only routes to a human when it genuinely doesn't know.

GPT-5PineconeHubSpotExpress
Industry
SaaS Design · United States
Team size
25–50
Duration
8 weeks
Launched
2025

Member's Services Chat Agent

30% fewer tickets escalated to a human50% fewer tickets escalated to a human

Problem. A members-only services team buried in repeat questions about benefits, booking rules, and policy edge cases.

Approach. Built a knowledge layer from membership policies, benefit matrices, and booking workflows. The agent handles routine member requests inside the existing support platform; complex cases route to specialists.

GPT-5PineconeFreshdesk
Industry
All Inclusive Resort · Caribbean
Team size
1000+
Duration
10 weeks
Launched
2025

Technical Support Voice Agent

50% faster response time on inbound technical issues80% faster response time on inbound technical issues

Problem. Technical support calls bottlenecked at intake — engineers spent the first 15 minutes diagnosing and routing instead of solving.

Approach. Built a knowledge layer from technical runbooks, product documentation, and escalation paths. The voice agent validates the issue, routes it to the right engineering team, and documents the call in Autotask before a human picks up.

Claude HaikuAmazon BedrockAmazon ConnectAmazon PollyAutotask
Industry
Cloud Infrastructure Distributor · Dominican Republic
Team size
50–100
Duration
12 weeks
Launched
2026

The same method, extended

From support agents to a fully-automated pipeline.

The knowledge-layer approach isn't limited to support. Here it drives an end-to-end content production pipeline — the same method applied to a new pattern.

Fully-Automated Short-Form Video Content Pipeline

$8.00 and 40 minutes per video$3.00 and 15 minutes per video

Problem. Producing short-form videos by hand doesn't scale: every clip needs a script, voiceover, music, captions, an avatar, and a thumbnail — then reformatting for four platforms. Cadence collapses under manual editing, and there's no per-channel visibility into cost or performance.

Approach. A script-driven pipeline turns one markdown spec into finished videos end to end: parse the timeline, AI voiceover with word-level timing, lip-synced avatar, music bed, assembly, word-by-word captions and overlays, thumbnail, then publish to four platforms. Checkpointed re-runs skip finished steps, with a metrics dashboard reporting cost and performance per channel.

PythonffmpegElevenLabsHedraRemotion 4Cloudflare R2Next.js dashboard
Industry
Cruise Excursions Platform · United States
Team size
10–25
Duration
6 weeks
Launched
2026