Knowledge graph grounded retrieval, not flat embedding search
Queries can be relational and evidentiary. Teams can ask for connected facts and traceable context rather than only nearest-neighbor similarity results.
AIME product 01
AIME.Retrieve
For media archive owners and rights holders who need decades of multimodal archive content to become truly searchable and reusable, AIME.Retrieve is an agentic, knowledge-graph-grounded retrieval system that turns unstructured archives into evidence-assembling, queryable knowledge on the customer’s own infrastructure.
Unlike cloud-API-first video AI offerings, it keeps archive data sovereign. Unlike long-cycle infrastructure initiatives, it is packaged as a deployable product path now, grounded in active research and implementation work.
Value proposition
Archive intelligence that reasons, not only retrieves.
AIME.Retrieve packages AIME's agentic multimodal retrieval research into a productized retrieval capability: multimodal ingestion, knowledge-graph construction, knowledge-graph-augmented retrieval, and agentic search orchestration. The result is a system that can assemble evidence across entities, relationships, and time, instead of returning isolated similarity matches.
Deployment is sovereignty-preserving by design. Raw archive materials stay within the customer boundary while retrieval workflows can still support modern AI-assisted newsroom and archive operations.
AIME.Retrieve differentiates on quality, cost, latency, and sovereignty: benchmarked retrieval quality, lower token cost from knowledge-graph grounded reasoning, agentic search that resolves multi-hop queries efficiently, and deployment that keeps archive data within the customer's own infrastructure.
The problem
Why current archive search still breaks under real editorial pressure.
- Existing archive search remains heavily keyword and metadata based, so evidence discovery across relationships and time depends on scarce manual archival expertise.
- Many multimodal search offerings are cloud API first, creating unresolved data sovereignty and IP licensing concerns for archive owners.
- Large standards and consortium efforts address system-wide infrastructure, but they do not hand individual archive institutions a deployable retrieval product today.
Differentiators
Six design choices that define AIME.Retrieve.
Agentic search orchestration
A reinforcement-learned search agent performs multi-turn, multi-hop retrieval and evidence assembly across archive structures that simple one-shot queries miss.
Sovereignty preserving deployment
The system runs on customer infrastructure. Only derived retrieval interactions cross boundaries, while raw archive content remains private by design.
Compliance embedded architecturally
Rights metadata, legal constraints, and policy enforcement are integrated directly into retrieval pipelines rather than patched in after model deployment.
Benchmarked, not only demoed
Retrieval quality and practical usefulness are treated as first-class workstreams, with evaluation design built into ongoing product evolution.
Smaller models, correctly reasoned answers
Knowledge-graph grounding lets smaller, cheaper models do the reasoning work instead of relying on ever-larger context windows, keeping token cost down while also handling temporal questions, such as what changed, when, and in what order, more correctly than flat similarity search.
Proof points
Credibility grounded in applied research delivery.
Built on AIME's retrieval-research architecture
Multimodal ingestion, knowledge-graph construction, knowledge-graph-augmented retrieval, agentic search, and benchmarking are carried forward as one integrated product path.
Target customer
Who this is for.
Fit
Media companies, public broadcasters, and archive institutions with large multimodal collections that must remain sovereign, yet need those archives to be searchable and reasoned over without sending raw content to third-party cloud APIs.
Next step
Evaluate AIME.Retrieve against your own archive constraints.
We can scope a research-grounded deployment path, data-boundary model, and evaluation setup with your archive and editorial teams. Performance figures and commercial packaging are confirmed per implementation context.