Overview
Ask SolE is an internal reference implementation used to validate how organizations can design, control, and deploy AI systems responsibly in production — with clear guardrails, grounded answers, and cost controls.
Context and constraints
As tools like ChatGPT and Gemini became mainstream, many organizations found that raw AI capability was not the problem. The real problem was operationalizing AI: aligning it to business intent, brand voice, and compliance boundaries while keeping reliability and operating cost predictable.
How we approached it
Designed and engineered a modular AI service with policy enforcement, retrieval grounding, rate limits, and human escalation — intentionally separating model calls from governance so the system remains predictable, auditable, and controllable.
What we built
- A backend-first AI service exposed as clean APIs, decoupled from the UI so multiple clients can reuse the same intelligence layer.
- A policy/guardrails layer that defines allowed intents, disallowed requests, and safe response behavior.
- Retrieval grounding with curated sources to reduce hallucinations and keep answers verifiable.
- Quota, rate limits, and usage tracking to prevent cost runaway and abuse patterns.
- Human escalation and deflection paths so the system can safely say “I don’t know” or route to a person.
- A modular structure to plug in tools/integrations without mixing governance logic into prompts.
Key capabilities
- Guardrail-driven behavior aligned with business and compliance expectations.
- Grounded responses from approved knowledge sources rather than open-ended generation.
- Cost controls (limits, quotas, and monitoring hooks) built into the execution path.
- Extensible architecture to adapt for support, internal knowledge, and advisory workflows.
- Human-in-the-loop patterns for sensitive or high-stakes interactions.
Implementation notes
- Built as a reference architecture to demonstrate repeatable patterns, not a “single prompt” demo.
- Separation of concerns between reasoning, governance, retrieval, and integrations to keep the system maintainable.
- Designed for production realities: failure modes, throttling, safe fallbacks, and observability hooks.
Outcome notes
- Validated a practical blueprint for production AI that emphasizes system design over model choice.
- Demonstrated how guardrails + retrieval grounding materially improve reliability and safety.
- Established reusable patterns that can be customized for client-specific AI initiatives.
Key outcomes
Technologies