Research & Development

Problem Space

LLMs still fabricate analytic claims, especially when summarizing complex datasets or generating insights from multiple data sources. These hallucinations can lead to incorrect business decisions and erode trust in AI-powered analytics platforms.

Research Goals

• • Trust-score API for real-time confidence assessment
• • Dashboard badge that flags uncertain charts and insights
• • Fine-tuned "hallucination-minimizer" model

Methodological Directions

Sentinel-RAG combines semantic-entropy probes with a RAGTruth-style labeling pipeline to assign a trust score to every EdenLM answer. Deploy chain-of-verification prompting and cross-reference results against source data fingerprints.

Practical Value

Business users gain transparent confidence indicators on AI-generated insights, enabling informed decision-making. Data teams can audit and trace every claim back to its source, meeting compliance requirements for reporting.

Problem Space

Even state-of-the-art text-to-SQL models hallucinate joins, swap units, or apply the wrong aggregation. End-users rarely notice until a board deck shows the wrong revenue trend.

Research Goals

• • Dual-agent protocol with author and critic agents
• • Schema-and-constraint awareness with sandbox execution
• • Confidence scoring for UI decision making

Methodological Directions

Pair generative models with static-analysis tools such as sqlfluff and data-validation libraries. Maintain a test-set of business-critical queries with ground-truth answers.

Practical Value

Non-technical executives can rely on conversational dashboards for shareholder reports. Internal analytics teams spend less time code-reviewing ad-hoc SQL.

Problem Space

The "long tail" of SaaS and on-prem tools still needs custom ETL jobs. Each manual build delays a sale, inflates professional-services hours, and locks smaller vendors out.

Research Goals

• • Spec-to-code generation from OpenAPI/GraphQL
• • Self-testing harness with unit and integration tests
• • Crowd-vetted registry with version constraints

Methodological Directions

Use chain-of-thought prompting that plans extraction strategy, writes code, then writes pytest cases. Employ RL-HF on historical connector data.

Practical Value

EdenLM Data widens its addressable market to niche vertical tools within days, not quarters. Systems integrators resell the platform with far lower marginal cost.

Problem Space

Permits, benefits, and reports often require manual data entry across disconnected systems. Backlogs can stretch to weeks, while citizens expect consumer-app speed.

Research Goals

• • Document triage with classification and extraction
• • Eligibility and rules engine with declarative policy logic
• • Central audit trail for inspectors and ombudsmen

Methodological Directions

Fine-tune small-footprint models on local language and form layouts. Integrate with low-code RPA tools. Run A/B pilots measuring time-to-decision and citizen NPS.

Practical Value

Municipalities clear backlogs faster, improving business climate. Citizens see transparent, traceable decisions without expensive IT replacements.

Problem Space

Delegating high-stakes decisions to opaque algorithms is politically and ethically untenable. Regulatory pressure demands demonstrable oversight and accountability.

Research Goals

• • Plan-submit-approve pattern for irreversible actions
• • Policy modules as composable YAML configurations
• • Immutable ledger with Merkle tree verification

Methodological Directions

Extend open-source libraries with context-aware "policy prompts." Provide diff viewers for human modifications. Simulate adversarial scenarios to stress-test.

Practical Value

Ministries can adopt agentic AI while meeting procurement guidelines. Private enterprises gain defensible compliance artifacts for auditors and insurers.