Media & Entertainment · Archive digitization

A dark archive is a monetization problem, not a storage problem

Banao digitizes broadcast archives — tape, film, and legacy file formats — and builds a clip-level search index that producers, rights teams, and schedulers can query. The archive stops being a vault and starts being a working library.

The system combines OCR, speech-to-text, and computer vision over your footage, then wires the metadata into your existing MAM or CMS. No proprietary lock-in; the index is yours.

What a Banao archive digitization project delivers

Digitization without indexing produces a tidy hard drive, not a usable library. We build both — the ingestion pipeline and the search layer.

Speech-to-text transcription at scale

Audio tracks across your archive — interviews, commentary, narration — converted to full-text transcripts, timestamped to the second, so producers search by spoken word, not filename.

Computer vision scene and shot tagging

Vision models scan every frame for faces, locations, objects, and on-screen text, attaching structured labels at clip level. Rights teams know what is in the library without watching every tape.

OCR over titles, slates, and lower-thirds

Text burned into the frame — episode slates, lower-thirds, broadcast dates — extracted and indexed alongside the audio. A producer searching by reporter name or broadcast date finds the clip immediately.

Format and codec handling for legacy media

Banao's pipeline handles DigiBeta, U-matic, mixed MP4 and MXF pools, and tape-transfer outputs without requiring re-encoding up front. The ingestion layer normalises on the way in.

Clip-level search and preview interface

The end output is a search bar your team actually uses — full-text, filter by date, speaker, topic, or location — returning timestamped clips with thumbnail previews, not just asset filenames.

MAM and CMS integration

Index and metadata land directly in your existing media asset manager or content management system. We do not require a new archive platform — we extend what you already run.

We build on what we run, before we sell it

Banao runs a ~300-person engineering operation on its own AI products. InterviewGod handles our own hiring pipeline. Vikaas runs our own demand generation. We know what it means to depend on a system that has to work every day.

An archive pipeline that Banao builds has to be maintainable by our own engineers, not just demonstrable in a vendor deck. That standard — build it as if you will run it yourself — shapes every delivery.

  • InterviewGodScreens Banao's own engineering hires every week.
  • VikaasRuns Banao's own demand-gen pipeline end to end.

When archive AI is the wrong starting point

We will tell you before you spend on it:

  • No digitization budget: if the tapes have never been transferred to digital, the first spend is hardware and capture, not AI. We can advise on that sequence, but it is a separate project.
  • Very small archive: under a few hundred hours, a cataloguing coordinator costs less than a trained pipeline. We will say so and point you to the right resource instead.
  • Poorly recorded audio: if the speech signal is degraded or multi-track production was never separated, transcription accuracy degrades significantly. The Discovery Sprint surfaces this before you commit.

How we start — two weeks before you commit to anything

Archive projects can run for months. We begin with a two-week sprint that tells you whether the ROI is there and exactly what the build requires.

  1. AI Discovery Sprint2 weeks · fixed price

    We ingest a representative sample of your archive — mixed formats, difficult tapes, your worst-case material — and run the full pipeline over it. You receive a clip-level accuracy report, a MAM integration assessment, and ROI maths based on your actual search and licensing volumes. Yours to keep; proceed and the cost is credited against the build.

  2. Build

    Full pipeline across your archive — speech-to-text, vision tagging, OCR, format handling — with the search interface and MAM integration as co-deliverables. We handle the data engineering; your team owns the metadata.

  3. Production & enrichment

    Live deployment with a producer-facing search interface and a dashboard showing coverage, confidence scores, and gap areas. New acquisitions route through the same pipeline automatically.

Frequently asked questions

We work with tape-transfer outputs — once the signal is captured to file, our pipeline takes it from there. For the physical capture step itself, we can advise on vendors and spec the transfer requirements so the files arrive in a format the pipeline can use cleanly.

Accuracy depends heavily on audio quality, compression artefacts, and the recording language. The Discovery Sprint runs your actual material — not a clean test sample — and returns accuracy figures by content type. Where transcription accuracy falls short, we scope a human-review step into the workflow rather than publishing noisy metadata.

Our integration layer covers common broadcast MAMs and handles custom API work for others. The Discovery Sprint includes a MAM integration assessment so there are no surprises in the build phase.

You do, entirely. The index and all structured metadata export in standard formats and land in your own storage and MAM. There is no vendor lock-in and no ongoing licence fee tied to the metadata itself.

A 2-week Discovery Sprint, followed by a 6–8 week build pipeline, then parallel ingestion running at your archive's transfer rate. For large archives, ingestion continues in the background while producers start using the search layer on what is already indexed.

Find out what your archive is actually worth

Bring your most inaccessible material and your current search and licensing request volumes. In 45 minutes we will map the ROI case and what a clip-level search pipeline would take to build.

Book a 45-min scoping call