Agentic platform vision for messy catalogs

A future where catalog search gets tuned to your domain, not forced into vendor defaults.

The vision is an agentic catalog-search tuning platform for mid-market teams that do not have spare search, product, and ML bandwidth. You bring a catalog and real customer queries. Agents help benchmark, diagnose failures, tune the stack, and hand back a search endpoint that fits the domain.

Request a benchmark Read the 11-stage playbook

Who this is for Mid-market catalog businesses and non-technical founders losing search-to-conversion without a dedicated search team.

Why now Embeddings, rerankers, and LLM agents moved the technical baseline. The bottleneck is engineering bandwidth, not model capability.

Reference proof Indian auto parts was the first proving ground because it is one of the worst-behaved search surfaces anywhere.

Product direction The auto-parts endpoint is the wedge. The real product is the repeatable tuning system behind it.

Proof, not pitch

2×

better search quality than the default SaaS setup on our first hard-domain experiment

0.451 Current tuned-system quality score on the auto-parts benchmark

44 / 149 queries where the default setup failed to return useful results

0 / 149 queries with zero results after tuning

+0.215 extra lift on misspelled searches after reranking

Auto parts is the reference implementation. The point is not “we built an auto-parts site.” The point is that a hard domain can now be tuned systematically.

The Business Problem

Most teams are not bad at search. They are under-resourced for it.

Search is a revenue surface, but most mid-market teams ship vendor defaults because deep tuning takes product, engineering, and ML bandwidth they do not have.

Default SaaS assumes clean input

In India, customers switch between Hindi, Romanized Hindi, and English, often inside the same query. Vendor defaults were not designed for that reality.

Catalogs have domain-specific mess

Misspellings, symptom queries, part numbers, and brand-as-generic behavior are not edge cases. They are the workload. Good search needs domain adaptation, not one generic ranking rule.

The opportunity is operational

The technical pieces already exist. The gap is stitching them into a repeatable workflow that a smaller team can actually use.

Proof

Auto parts is the proof that the playbook can work

We chose Indian auto parts because it is an unusually hard search domain: multilingual, misspelling-heavy, symptom-first, and full of brand drift. If the workflow can produce a meaningful lift here, it earns the right to be applied elsewhere. This benchmark is the first evidence point.

What we measured	Default SaaS	Tuned system	Research ceiling
Overall search quality	0.23	0.45	0.47
Hindi / Hinglish queries	0.14	0.47	0.54
Misspelled queries	0.20	0.76	0.53
Symptom-based queries	0.14	0.50	0.55
Queries with zero results	44 / 149	0 / 149	—

Default SaaS here means the normal out-of-the-box setup most teams start with. The point is not that these vendors are bad. The point is that hard catalogs need more tuning than most teams can do alone.

Benchmark chart comparing default SaaS search, our tuned search, and OpenAI ceiling

What The Product Does

Not “AI search.” A workflow that turns messy catalogs into better search.

The vision is a platform that turns search tuning from an expert-only project into a guided, benchmark-backed workflow.

What a customer would get

A search benchmark built around real customer queries
A diagnosis of where the default setup is losing conversions
A tuned search configuration for the domain
A working endpoint with a clear before-vs-after story

Why we believe this can work

We already tuned one very hard domain end-to-end
The process is benchmark-first, not vibes-first
The workflow mixes retrieval, routing, and reranking instead of betting on one trick
The technical stack is documented and replayable

How it scales

The auto-parts demo matters because the process is replayable

The long-term product is not this one domain. It is the repeatable, agentic workflow that can be replayed on other hard catalogs.

Catalog + query intake Start with the business reality: product rows, real queries, failure cases, and category nuance.

Benchmark and error taxonomy Build the evaluation set, classify why default search breaks, and measure against the right baseline.

Tune retrieval, routing, and ranking Adjust the stack to the catalog instead of pretending one generic vendor setup is enough.

Ship the endpoint Hand back a scoped API and a benchmark narrative that proves why it is better than the default path.