Agentic NLP-to-SQL for energy intelligence

Potentia Natural Language Interface

A chat-native analytics cockpit that turns questions into verified SQL and immersive, live graphs.

Potentia fuses agentic routing, schema-aware prompting, Bedrock SQL generation, and guardrailed execution to deliver real-time visuals. It raises the competency level of every analyst, regardless of experience, with NLP-to-SQL tooling that ensures consistently high-quality output. Ask a question, watch the agentic pipeline build the query, then interact with the graphs to steer the next insight. Every step is designed for confident, fast exploration.

Agentic Orchestration Schema-Aware Prompts Natural Language Graph Studio Multi-Directional Flow
Natural Language Studio
Chat
Show me usage over the last month.
Routing to energy_usage. SQL validated. Generating time-series line chart + summary.
SQL SELECT hour_of_day, AVG(usage_kwh) AS avg_usage FROM energy_insights WHERE business_id = 'BIZ001' GROUP BY hour_of_day ORDER BY hour_of_day;
line chart suggest follow-ups save graph
Usage Over Time

Visualization hint: time-series line chart

auto
Time window Last 30 days
Avg daily usage 12.4 kWh
Confidence 0.95
Agentic Steps 10 Intent -> Route -> Schema -> Prompt -> Generate -> Validate -> Execute -> Analyze -> Visualize -> Iterate
Graph Types Line, Area, Bar, Scatter, Pie, Heatmap, Radial, Table Auto-selected with overrides in chat

Why this NLP-to-SQL system stands out

Every component is tuned for trustworthy answers, fast iteration, and immersive data exploration.

Agentic intent routing

Every question is classified with confidence scoring so the right domain context is always in play.

Schema-aware prompting

Join hints, table descriptions, and few-shot examples keep SQL generation grounded and accurate.

Governed SQL execution

Read-only validation, retries, and policy checks protect data while still moving fast.

Dynamic graph studio

Visualization hints and shape detection drive automatic chart selection and interactive dashboards.

Chat-native exploration

Insights, summaries, and guided next steps keep the conversation focused and productive.

Multi-directional flow

Graphs feed new prompts, prompts reshape queries, and saved views keep exploration continuous.

Agentic NLP-to-SQL pipeline

A cutting-edge chain of reasoning that is transparent, auditable, and optimized for data confidence.

01

Intent capture

Natural language queries enter a conversational memory that preserves constraints and context.

02

Domain routing

Queries are routed to the right domain with confidence scoring and fast fallbacks.

03

Schema hydration

Table metadata, join hints, and business rules are injected before generation begins.

04

Prompt assembly

Few-shot examples and guardrails are blended into a domain-specific SQL prompt.

05

SQL generation

Bedrock models generate candidate SQL and refine it for clarity and performance.

06

Policy validation

Every query is validated for safety, read-only access, and schema compliance.

07

Execution + caching

Queries execute with performance limits and caching strategies for repeat explorations.

08

Result analysis

Automatic insights, anomalies, and summary statistics are generated for every response.

09

Visualization inference

Data shape drives chart selection so the right visualization appears instantly.

10

Feedback loop

Follow-ups, saved graphs, and overrides feed the next prompt for rapid iteration.

Observable by design

Every stage emits metadata: confidence, SQL, visualization hints, row counts, and insights.

Cutting-edge governance

Safety filters, schema enforcement, and retry logic keep answers trustworthy at speed.

Explainable results

Insights and suggested follow-ups turn raw output into a narrative you can act on.

Latency-aware orchestration

Parallel tools, timeouts, and retry budgets keep responses fast and predictable.

Deterministic reruns

Seeded prompts and cached SQL outputs make audits and regressions repeatable.

Safe fallbacks

If generation fails, the agent degrades to vetted templates or retrieval-backed queries.

Domain guarantees

Business rules, metric definitions, and join constraints prevent semantic drift.

Human override

Analysts can edit SQL, pin charts, and lock chart types when needed.

Lineage tracing

Every chart links to query text, model version, and source dataset.

Collaboration ready

Saved graphs, notes, and share links keep teams aligned on insights.

Interactive chat environment

The interface keeps the conversation and the visuals together so analysts stay in flow.

Ask, see, refine

The left panel captures intent, while the right panel auto-generates charts, tables, and insights. Follow-up suggestions are grounded in the current result set, making exploration feel guided and fast.

  • Suggested prompts based on query analytics and time ranges
  • Visualization overrides for line, bar, heatmap, scatter, and table views
  • Save graph actions to keep a curated trail of findings
  • Insight cards that summarize trends, ranges, and anomalies
Live Session Potentia Workspace
Compare usage in February 2025 vs March 2025.
Summary: March is +12% vs February. View: comparison chart.
Suggested follow-ups: weekday vs weekend, peak hours, cost by hour.
Feb Mar
MonthUsage kWh
Feb 20258,420
Mar 20259,430
domain: energy_usage rows: 2 viz: comparison_chart

Dynamic graph generation

Auto-detects the best visualization, then hands control back to the analyst.

Heatmaps

Perfect for usage by hour and day. Highlight peaks at a glance.

Trend lines

Summaries for weekly, monthly, and yearly trends with auto smoothing.

Comparisons

Side-by-side period analysis with clear deltas and annotations.

Stacked area

Multi-series usage layers that reveal baseload vs variable demand.

Scatter + correlation

Spot relationships, outliers, and anomalies in seconds.

Pie + radial

Distribution views for categories, rates, and efficiency mixes.

Load duration curves

Instantly visualize baseload, peak demand, and variability.

Tables

Raw outputs ready for export, validation, and audit trails.

timestampusage_kwh
2025-03-05324
2025-03-06318

Multiple data flow directions

Insights move forward into visuals and backward into new questions, keeping analysts in a continuous loop.

Ask
Route + Build SQL
Visualize
Refine
Agentic Loop chat -> sql -> graph -> chat

Forward flow

User intent becomes executable SQL, then turns into charts and summaries.

Reverse flow

Graph interactions and insights seed the next prompt and refine the scope.

Lateral flow

Saved graphs, shared views, and annotations keep teams aligned on the same narrative.

Intelligence stack for agentic analytics

Purpose-built components that keep the experience fast, safe, and deeply interactive.

Intent router

Matches every question to the right domain and carries forward constraints automatically.

Context composer

Hydrates prompts with schema, rules, and examples to improve SQL accuracy.

SQL governance

Validates and filters queries so results stay safe, read-only, and performant.

Adaptive visualization

Chooses charts based on data shape, then allows overrides and annotations.

Insight narrator

Summarizes trends, anomalies, and key deltas in human terms.

Saved graph workspace

Stores curated explorations so teams can revisit and share findings.

Ready to explore energy data at the speed of conversation?

Potentia pairs a powerful agentic NLP-to-SQL core with immersive graph exploration.