You were asked to find the tools that improve the business, and this is the whole engineering answer
We look at the business process holistically and solve several use cases on one product intelligence layer, rather than handing you one more point tool to evaluate, integrate and defend.
Most enterprise AI is blind to your hardest content
In semiconductors, electronics, manufacturing and infrastructure, the critical knowledge often lives inside images buried in long PDFs, not in paragraphs.
Visual Reasoning Engine
Conventional retrieval flattens documents into text at ingestion and discards diagrams and structured visuals. Specifications embedded in schematics, timing charts, mechanical drawings and configuration screenshots never reach retrieval at all. We reconstruct technical visuals with layout and structural fidelity and index diagrams, tables and text in one multi-modal retrieval framework.
Grounded answers, cited
Every output traceable back to the document it came from. Not a confidence score, a citation your engineers can open and check.
Block diagram generation
System architecture built from a described use case, with components named from your own catalog.
Institutional knowledge
The answers currently held by a handful of long-tenured engineers, captured into a system that can answer with them.
LLM-as-judge
Automated quality assessment in the loop, so answer quality is measured continuously rather than sampled occasionally.
A practical evaluation framework
Built to run against real traffic, not a benchmark you pass once at procurement and never revisit.
In deep technical domains, diagrams are not decoration, they are the specification. When a critical detail lives in a schematic or an engineering drawing, the AI has to be able to interpret that visual directly.
What you would have to build
This is the honest version of the build-versus-buy conversation. Here is the list, so you can price it properly.
Ingestion that scales
A pipeline built to take in hundreds of thousands of documents, datasheets, application notes, tickets, schematics, catalogs, and keep them current as they change.
Multiple agents, multiple knowledge hubs
Different agents for different audiences, each scoped to what that audience is permitted to see. The boundary is structural, not a prompt instruction.
MCP servers
So the same product intelligence serves use cases beyond the agents we ship, including the ones your own team wants to build on top.
The moment it goes public, the threat model changes completely
In a private dashboard, users are authenticated employees asking legitimate questions. In a public community with thousands of developers, anyone can interact with your agent, and some will try to make it say things it should not.
- Some problem queries carry no explicit toxicity signal at all: they are semantically off-topic rather than overtly harmful, which makes them poor candidates for hard blocking at the input layer.
- Blocking aggressively on that basis risks flagging legitimate questions that happen to touch a social topic in passing.
- The downstream pipeline does not need to detect toxicity. It asks a simpler question: is this answerable from the customer’s knowledge base?
- When the answer is no, the agent responds within its domain scope.
- Same protection, without the false-positive cost of an overly aggressive input filter.
Common questions from AI and IT leaders
Why should we not build this ourselves?
Some of it you could. The list that is hard to sustain is: a multi-modal ingestion pipeline that preserves diagrams, a normalized product knowledge graph that stays current, permissioned hubs, an evaluation framework running against live traffic, a layered safety filter that does not destroy the false-positive rate, and the domain work of getting all of it right for electronics specifically. Most internal builds get to a working demo quickly and stall on the rest.
How do you evaluate answer quality over time?
With a practical evaluation framework that runs against real traffic rather than a fixed benchmark, plus LLM-as-judge in the loop. We also review conversations with customers directly, because the most useful signal is usually a specific answer that went wrong and the reason it did.
Can we access the underlying intelligence, not just the agents?
Yes. We expose MCP servers so your own teams can build on the same product intelligence layer, which is a common request once the first deployment proves out.
How is confidential or pre-release material kept out of public answers?
Multiple agents across multiple knowledge hubs, scoped by audience. Material that should not be public is not in the public agent’s hub, it is an architectural boundary rather than an instruction the model is asked to respect.
What about hosting and infrastructure?
Our core platform runs on Google Cloud, we benchmark models continuously and can run on other clouds. Where customers have specific infrastructure requirements we work through them directly.
What does your security review look like?
We are SOC 2 Type II certified and maintain a trust center. Most enterprise reviews we go through are completed from the material there plus a call with our team.
Related
Bring us your hardest documents
Send the PDFs where the answer only exists inside a diagram. That’s the fastest way to find out whether this is different from what you’ve evaluated already.
SOC 2 Type II · Every answer traceable to its source