// Case Studies

Real problems.
Shipped with AI that works.

We partner with teams to go from prototype to production. Here's what that looks like in the wild.

bards.ai team in conversation at an industry event

  • 10x

    reduction of human workload

  • 92%

    annotation accuracy on first pass

  • <100ms

    inference rate

AI-Powered Video Annotation

Auto-Annotating UI Elements for VOD Apps at Scale

We built an AI-driven tool that automatically identifies and pre-annotates UI elements in Comcast's VOD applications, replacing a manual QA process that couldn't scale.

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  • 25

    hours saved per SDR / week

  • 500+

    meetings booked per quarter

  • Seamless

    integration with Chili Piper SDR

AI Sales Automation

Automated Prospect Form Grabber for Sales at Scale

An AI-powered system that identifies forms on web pages, fills them intelligently, and captures submissions - eliminating hours of manual data entry for Chili Piper's SDR team.

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  • 200

    live cameras per on-prem server

  • ~88%

    less time per incident review

  • ~33K

    residents covered (Oława)

AI Video Search

Text-search across 200 live city camera feeds

Municipal operators type a description and the system surfaces matching events from across the city's live CCTV network. We built it for Neural; the City of Oława's Straż Miejska runs it on-prem. 200 cameras per server; review time on a typical incident dropped from ~8 hours of manual scrubbing to under 1 hour - an ~88% reduction.

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  • 300B+

    tokens processed

  • 100k+

    credits sold in 6 months

  • 5 months

    from concept to full product release

LLM Content Generation

Production LLM Processing at Surfer Scale

We helped Surfer handle massive content generation workloads with a reliable, cost-optimized LLM pipeline built for scale.

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  • 50×

    cheaper per 1000 requests

  • 3×

    lower end-to-end latency

  • 98.3%

    F1 retention vs. frontier-API baseline

Custom Fine-tuning

Fine-tuned a small model to frontier quality - 50× cheaper at high volume

Customer's frontier-API entity-extraction pipeline worked but the per-token bill was eating margin at the volume they wanted to ship at. We split the task into hybrid retrieval + two fine-tuned Gemini 2.5 Flash Lite models. 98.3% F1 retention on the customer's existing eval suite, ~50× cheaper per 1000 requests, ~3× faster - without touching the prompt or the eval.

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