Relationship-grounded context
Give the model less to read and more to understand.
MCP makes enterprise systems accessible to an agent. Flaibot resolves the relationships first, so the agent receives the smallest governed context needed to answer accurately.
ILLUSTRATIVE BENCHMARKsame model · same query · same sources
Claude + MCPBroad retrieval
57k
median input tokens
35% fewerrelationship-grounded context
Claude + MCP + FlaibotResolved context
37k
median input tokens
Interactive model
Change the workflow. Watch the context shrink.
This calculator illustrates how record volume and relationship resolution affect the amount of context sent to the model. It is not a measured customer result.
ASSUMPTION
Average retrieved record contributes 315 input tokens before deduplication, policy filtering, and relationship traversal.
Broad MCP context
56,700
Flaibot context
36,855
TOKEN REDUCTION35%
ILLUSTRATIVE LATENCY8.3sfrom 12.8s
CONTEXT QUALITYsource-linkedsame answer threshold
Why the difference exists
Flaibot resolves before the model retrieves.
Same question. Different context.
Access is not understanding.
customer_1042.json
customer_1042_copy.json
claim_7781.pdf
claim_notes_final.docx
claim_notes_final_v2.docx
policy_2024.pdf
policy_2025.pdf
provider_export.csv
...The model must infer identity, relevance, duplication, and policy from the payload itself.
ENTITY: Customer 1042
RELATION: filed Claim 7781
POLICY: 2025 plan applies
EVIDENCE: 7 permitted sources
CONFLICT: none
LINEAGE: complete
DECISION PATH: source-linkedThe model receives a resolved relationship path plus the evidence needed to support it.
Common questions
What teams ask about context efficiency.
Does Flaibot replace MCP?
No. MCP exposes tools and systems. Flaibot adds the governed relationship context that helps an agent use those tools more selectively.
Does fewer tokens mean less evidence?
No. The goal is to remove duplicated and irrelevant context while retaining the authoritative evidence and lineage required to support the answer.
Is Flaibot tied to Claude?
No. The same context layer can ground Claude, ChatGPT, Copilot, or another enterprise model and agent interface.
How should the benchmark be validated?
Use a representative question set, fixed source systems, identical permissions, and a quality threshold that measures citations and unsupported claims.
Use your own workflow