Why Supper · Agentic data infrastructure

Your AI agents are only as good as the data layer underneath them.

Connecting Claude to your databases and SaaS tools via MCP works in a demo. In production — multiple sources, complex questions, a growing skill library, a team that actually relies on the answers — it breaks in five predictable ways.

4–32×
Token cost of an MCP-heavy stack vs. a governed semantic layer
100s+
Per multi-source question — five sequential API round trips
16–25%
Query accuracy on real enterprise schemas with no semantic layer
→0%
Accuracy by the fourth chained, cross-source question
50+
Skills to hand-build and maintain as your stack grows

What Supper delivers

Supper gives your agents one governed layer — accurate answers, a fraction of the cost, and nothing to maintain.

4–32×
Cheaper than an MCP-heavy architecture at scale
<10s
From a plain-language question to a verified answer
>95%
Answer accuracy on the governed semantic layer
1
Resolved query path, however many sources it spans
0
Connectors for your team to build or maintain

01 · Cost

The token bill is larger than you think

Two components compound with every question and source: the context tax before your prompt runs, and the response bloat of data returning through the context window.

Cost item
DIY MCP stack
Supper
Tool-definition overhead / session
50K–200K tokens before prompt5–10 MCP servers · typical enterprise
Pre-compiled semantic contextCached · not re-loaded per call
Query-result return
Results into context windowThousands of rows = 50K–200K tokens
Out-of-band deliveryResults don't bloat the context
Multi-turn conversation bloat
Context re-processed every turn20-turn session: $3.30+ static overhead
Token-efficient architectureComplexity doesn't multiply cost
Estimated monthly cost at scale
$1,000s–$81,000+/moOne real analysis: $81K/mo overhead
4–32× cheaperOnlyCLI benchmark · 2026

That's the explicit cost. The implicit cost is your team — every hour spent building skills, rebuilding after an API change and debugging wrong answers is an hour not spent on real work.

02 · Speed

Waiting on your browser for Claude is not a workflow

DIY MCP / Claude skills
  • —Synchronous by design — you wait while the model works
  • —Multi-source queries chain tool calls — latency compounds
  • —Schema rediscovery every session adds overhead
  • —No scheduling — nothing runs unless you're there to ask
  • —Complex reasoning chains can time out before completing
SUPPER On your schedule
  • +~10 seconds average, question to verified answer
  • +Pre-compiled schema — no per-session setup overhead
  • +Multi-source queries routed through the semantic layer
  • +Workflows run on schedules — daily, weekly, any cadence
  • +Delivered to Slack, email or dashboards automatically

The real advantage isn't raw latency — it's that Supper works on your schedule. The Monday pipeline report, the Friday leaderboard, the monthly finance summary: validated and delivered before anyone asks.

03 · Accuracy

Without a semantic layer, your LLM is guessing

GPT-4 · enterprise SQL · zero-shot

16.7%

Average accuracy on real enterprise SQL databases. On high-complexity queries it was 0%. A knowledge-graph layer lifted it to 54% — still less than half what Supper achieves with a governed semantic model.

Source: “The Role of Knowledge Graphs on LLM Accuracy for Enterprise SQL” · arXiv 2311.07509

DIY LLM on raw schema
  • —Infers field meaning from column names — guesses
  • —Picks one of several conflicting metric definitions, silently
  • —No institutional knowledge — every session starts from zero
  • —Confident wrong answers look exactly like confident right ones
SUPPER Semantic layer
  • +Every metric definition encoded by your data team, applied consistently
  • +Your ARR formula, your churn definition — never guessed
  • +Schema compiled and cached — no rediscovery, no inference
  • +Every answer shows its reasoning and the underlying query

Systems that reach 70–85% without a semantic layer expose only a handful of curated views and force the LLM onto predefined metrics. That's a hand-maintained semantic layer by another name — or you use Supper's.

04 · Complexity

The first question works.
The fifth question breaks everything

Q1
“What was our MRR last month?”
Single source, single metric. The LLM finds the table, runs the query. Works fine.
LLM works
Q2
“Break that down by customer segment.”
Requires a second table. The LLM infers the join path — sometimes right, sometimes wrong. Your segmentation definition isn't in the schema.
Inconsistent
Q3
“Which segments have the highest churn risk this quarter?”
Now spans CRM, product usage and billing. The LLM doesn't know your churn definition — it picks one. Multiple valid join routes: it picks one. The answer looks right.
Likely wrong
Q4
“Compare that to last year's cohort in the same segment.”
Year-over-year cohort analysis across four sources. Context window filling, schema rediscovery competing with reasoning, compounding inferences. Accuracy near zero.
Breaks down

Supper's semantic layer doesn't degrade with complexity. The deeper the question, the more it earns its value — definitions, join paths and business logic are pre-resolved, not inferred on the fly. The fourth question is as reliable as the first.

05 · Resource drain

A skill library is a job — and it only gets bigger

Engineering time as the stack grows
Initial build · 2 sources
Manageable
Add 2 more sources
Growing
Cross-source skills
Heavy
API changes + rebuilds
Painful
50+ skills · 6+ sources
Full-time job

The cost isn't front-loaded — it compounds as your data stack grows.

DIY Maintenance burden
  • —Every new source = a new connector build
  • —API changes break skills silently — wrong answers, not errors
  • —No monitoring — you find out when a user complains
  • —Knowledge lives in whoever built the skill
SUPPER Maintenance model
  • +All connectors built and maintained by Supper's team
  • +API changes caught and resolved before they reach users
  • +Answer accuracy monitored continuously
  • +Business logic encoded once, applied everywhere, forever

A real example

“How has ARR evolved for mid-market over the past year?”

A simple, reasonable business question. Here is the bill for answering it once — through Claude + a Looker MCP connection, versus through Supper.

Claude + Looker MCP
itemized · per question
get_models()42K tok · 21s
get_explores()38K tok · 16s
get_dimensions()49K tok · 28s
get_filters()36K tok · 14s
query()45K tok · 24s
schema rediscoveryevery session
retry on wrong turn+1–3 trips
5
Trips
210K
Tokens
~103s
Time
Supper
itemized · per question
natural-language intentincluded
schemapre-compiled
semantics · ARRpre-defined
filters · mid-marketpre-set
query + answer1 pass
1
Trip
<10K
Tokens
<10s
Time

Same question. Two very different bills — and the DIY one grows every time a source changes, a team scales, or an agent queries on a loop. Everything above compounds into this single line item.

The smarter backbone for your agentic data stack.

More accurate. More efficient. Less to maintain. Supper gives your agents reliable, governed data to work with — and your team the time back.

Talk to our team See how Supper works