Product & Hardware Engineering
An agent that knows your whole design
Arango turns design specs, datasheets, RTL, traces, and version history into one connected knowledge graph — so engineers and AI agents resolve issues with answers grounded in the real design hierarchy, traceable from requirement to implementation.
Value Drivers
What an Arango engineering agent delivers
01
Resolve issues faster
Answers grounded in the design hierarchy — spec → module → signal — not lookalike text. Engineers get the right module the first time.
Right answer, fewer dead ends
02
Precise context, 92% fewer tokens
HybridRAG returns a precise subgraph instead of a 5,000-token cloud — cutting LLM cost ~10× while making answers faster and more accurate.
5,000+ → <500 tokens / query
03
Spec-to-implementation traceability
Every link from requirement to implementation is typed, deterministic, and auditable — with a Graph Visualizer trail engineers can walk.
Auditable · reproducible
04
Preserve institutional knowledge
Temporal lineage, similar-situation detection, and Bus-Factor mapping surfacing design history and single points of failure automatically.
Surface risk · reuse IP
Metrics from Arango’s open-source IC hardware design knowledge graph (~180k edges, five RISC-V cores); results vary by dataset.
The Evidence
Proven on real designs
On Arango’s open-source IC hardware design knowledge graph — ~180,000 edges across five RISC-V cores — HybridRAG cut LLM context by 92% (5,000+ tokens to under 500), ran hybrid queries in under 60ms, and automatically surfaced cross-repo design lineage that previously lived only in senior architects’ heads. The same pattern applies wherever complex products are designed — semiconductors, automotive, aerospace, and industrial systems.
92%
fewer tokens per query
<60ms
hybrid query @ 180k edges
10×
token efficiency
1 engine
graph + vector + search
The takeaway: from a 5,000-token guess to a precise, traceable answer — every link from spec to implementation, auditable.
How it Works
Every design artifact, connected — and queryable
The Matrix
From engineering challenge to measurable value
| Engineering challenge | Arango Contextual Data Platform | Value · KPI / OKR |
|---|---|---|
| Vector-only RAG retrieves unrelated docs that share vocabulary | HybridRAG grounds retrieval in the design graph — graph + vector + full-text in one AQL pass | Right module first · fewer fabricated answers |
| Over-retrieval bloats the LLM context (5,000+ tokens) | Topological filtering returns a precise subgraph (<500 tokens) | 92% fewer tokens · ~10× efficiency · lower cost |
| Graph DB + vector DB = network hops & app-side joins | One engine; vector, graph & full-text execute in a single pass | Sub-60ms at 180k edges · no integration tax |
| Designs evolve over years; lineage & rationale are lost | Temporal graph — epochs, versions, cross-repo lineage | “What evolved from what,” auditable |
| Critical knowledge locked in a few experts | Bus-Factor mapping + similar-situation (“déjà vu”) detection | Surface single points of failure · prevent repeats |
| No traceable link from spec to implementation | Typed, deterministic spec→implementation graph + Graph Visualizer audit trail | Auditable verification · reproducible |
The Shift
Before and after Arango
Before
Vector-only, Fragmented
After
Arango Engineering Agent
Outcome
Right answer first
92% fewer tokens
Sub-60ms queries
Knowledge that outlives the team
Resolve issues at the speed of one query.
Point Arango at your specs, RTL, and version history — and give your engineers and agents grounded, traceable answers across the whole design.