AI for manufacturers, distributors and industrial suppliers
The short answer
Industrial buyers search by fitment, specification and problem, not by product name, and their suppliers answer from catalogues built for a warehouse rather than a customer. Branemind builds retrieval and agents that close that gap, plus field advisory agents that work where connectivity does not.
Who we usually work with here
- Sales or Distribution lead
- Accountable for quote turnaround and win rate.
- Product or Catalogue lead
- Accountable for specification data quality.
- Field operations
- Accountable for technician productivity in the field.
Where it actually breaks
The catalogue is written for the warehouse
Part numbers and internal descriptions do not match how a customer describes the thing they need, so enquiries turn into phone calls.
Specification knowledge lives in three people
Fitment and compatibility sit in the heads of long-serving staff, which is a single point of failure and a bottleneck on every quote.
The field has no signal
Tools that assume a live connection fail exactly where the technician needs them.
Constraints that shape every build here
- A compatibility claim has to be traceable to a specification, not inferred
- Field tooling must degrade to something usable offline rather than to nothing
- Specification data quality bounds everything downstream
Where we would start
In order of how often it pays off, not in order of what is most interesting to build.
Catalogue and specification retrieval
Normalise the catalogue, retrieve hybrid, rerank hard, and walk the compatibility graph rather than guessing at it.
Production AI EngineeringField advisory agents
Voice-first, cache the knowledge rather than the conversation, and degrade to SMS rather than to a spinner.
Multilingual Voice AgentsControls we hold ourselves to
- Compatibility answers cite the specification they came from
- No answer where retrieval is thin, rather than a confident guess
- Offline behaviour defined and tested, not assumed
Published work in this industry
Case study
Search that understands a part, not just its name
Forty thousand SKUs, half of them near-identical, and buyers who search by symptom rather than part number. A reranker and a graph fixed what keyword search never could.
- Search-to-cart
- +41%
- Zero-result queries
- −78%
- Support tickets
- −1/3
- 'which part do I need'
Case study
Advisory agents for the field, where the signal drops
An agent that only works on good 4G is a demo. The interesting design constraint is what happens on two bars, in a local language, in the middle of a season.
- Usable on 2G
- Yes
- voice-first fallback
- Advisory response
- < 4s
- Repeat usage
- 3.4×/week
Questions we get asked
How does the agent avoid recommending an incompatible part?
By walking an explicit compatibility graph rather than inferring from text similarity, and by citing the specification behind any claim. Where the graph has a gap, the correct output is a question, not a recommendation.