Media & Entertainment · Automated video editing
Your editors still cut highlights frame by frame
Banao builds automated video editing pipelines that detect shots, extract key moments, and package social clips and promos from long-form footage — with an editor approval step before anything goes live.
The system runs inside your MAM and CMS, not as a side tool. It handles sports highlights, news packages, interview clips, and promo reels — reducing the time from raw ingest to publishable cut.
Times Internet— AI-driven content packaging across digital video properties.
The first call is free · 45 minutes · no obligation
What we build
What a Banao automated editing deployment includes
Cutting highlights is only one part of the problem. The pipeline covers ingest, scene understanding, packaging, and the handoff to your editors.
Shot detection and scene segmentation
Models that parse every frame for scene boundaries, camera cuts, action peaks, and crowd-noise spikes — the raw material for every downstream clip decision.
Highlight and key-moment extraction
Trained on your sport or format to identify the moments worth packaging — a goal, a key interview line, a breaking news beat — scored and ranked for editor review.
Social and promo clip assembly
Assembles candidate clips to target durations — 15s, 30s, 60s — with intro/outro logic, title card slots, and caption drafts, ready for an editor's final approval.
MAM and CMS integration
Clips flow directly into your media asset management and publishing systems. No separate export queue, no manual re-ingest — the cut lands where editors already work.
Human approval workflow
Every candidate clip goes to an approval queue before publishing. Editors confirm, reject, or adjust — the AI does the first pass, not the final call.
Feedback loop and model tuning
Rejections and adjustments feed back into the model. After a few weeks of real production use, the candidate quality rises to match your house style.
Receipts
Where this is running
Metrics shown dotted (··) are being verified for our case-study pack. Published only once confirmed.
AI clip packaging across digital video and news properties
Times Internet produces high volumes of short-form content for its news and entertainment platforms. Banao's automated editing pipeline detects and packages highlights from long-form footage, freeing the editorial team for the decisions that need their judgement.
Dogfooding
We operate production AI before you do
Banao runs a ~300-person engineering company on its own AI — before it reaches a client. InterviewGod screens our own engineering hires every week. Vikaas runs our own demand-gen pipeline.
The standard we apply to your automated editing pipeline is the standard we hold our own internal systems to: it has to survive real operations, not just a controlled demo. That is what gets shipped.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When automated editing is the wrong investment
AI does the first cut well. Here is when it does not earn its keep:
- Highly subjective formats: pure creative direction — a brand film, an editorial long-read — where every choice is taste-driven. AI can assist with rough assembly; it should not be your creative director.
- Tiny output volumes: if your team publishes fewer than a handful of clips a day, the model training and integration cost outweighs the time saved. We will tell you before you commission a build.
- Poor source quality: heavily compressed footage, missing audio metadata, or no timecode makes scene segmentation unreliable. The Discovery Sprint catches this before it wastes budget.
How we start
How we engage — prove cost before building
You have seen this pitched before. We start from what the problem is worth, not from a licensing brochure.
- 01
AI Discovery Sprint
2 weeks · fixed price
We ingest a sample of your real footage, run shot detection and highlight extraction, and hand back a candidate quality report and ROI maths — yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Model training on your format and house style, MAM/CMS integration, approval-queue workflow, and a clip dashboard. The data pipeline and output formats are part of the deliverable.
- 03
Production & continuous learning
Live deployment with editor approval workflow and feedback-loop tuning. The model sharpens weekly as your editorial team reviews and corrects candidate clips.
FAQ
Frequently asked questions
What formats and codecs does the pipeline support?
Banao's ingest layer handles the common broadcast and digital formats — MP4, MXF, MOV, ProRes, H.264, H.265 — and routes through your MAM's existing transcoding logic. Unusual formats are identified in the Discovery Sprint and resolved before the build starts.
Does the system need to be trained on our specific sport or show format?
Yes, and that training is part of the build. Generic highlight models exist but underperform on your format and house style. Banao trains on your labelled content, so the candidate quality matches what your editors would select.
How does the editor approval step work?
Candidate clips are presented in a review queue — your editors see the start/end frame, a confidence score, and a reason. They approve, reject, or adjust duration. Rejections feed the model; approvals publish via your CMS.
Can it handle live content, or only VOD?
Both, with different latency profiles. VOD processing runs in batch and delivers candidates within minutes of ingest. Near-live processing — match highlights ready 90 seconds post-event — is achievable but requires edge infrastructure. The Discovery Sprint scopes which applies to your use case.
We already have an NLE — does this replace it?
No. Automated editing handles the first pass: candidate clip selection and rough assembly. Your existing NLE workflow handles polish, graphics, and final sign-off. The two coexist; the AI feeds the NLE queue rather than replacing it.
Get started
See what a first-pass cut of your footage looks like
Send us a sample of your hardest format — a live event, a long interview, a full match. In 45 minutes we'll show you candidate clips and the ROI maths behind building it at scale.
Book a Discovery Sprint