# WebOracle — Alec Goldis, Fractional CTO & AI Advisor

Company: WebOracle LLC (brand: WebOracle). The site logo is a pixel-block graphic spelling "WEBORACLE".

> Fractional CTO and AI advisory services: LLM integration, MCP (Model Context Protocol) servers, RAG architecture, agent workflows, AI readiness assessment, and cloud/data platform architecture on AWS.

Alec Goldis is a consulting architect with 45 years of hands-on experience across frontend, backend, mobile, databases, desktop software, and industrial control systems (PLC). He can be engaged as an individual fractional CTO / AI advisor, or with a compact team of senior AI professionals that delivers complete projects end to end. Hands-on delivery, not slideware, not staff augmentation.

## Engagement models

- Fractional CTO / AI Advisor (individual): 3-5 months at 15-24 hours/week. Works with CEO, CTO, product and engineering leadership. Typical outputs: AI roadmap, architecture, governance model, vendor decisions, delivery plan, team mentoring. Best for ongoing leadership, transition, or strategic change.
- Senior AI Delivery Team: 3-7 weeks end to end. Defined project ownership, not staff augmentation — WebOracle does not sell developer capacity; it takes ownership of a defined outcome. Typical outputs: working production system, source code, deployment pipeline, evaluation harness, documentation and handoff. Best for contained, high-value initiatives with clear business ownership.

## When to bring WebOracle in

- Several AI experiments are running with no coherent production architecture behind them
- Executives need an AI roadmap grounded in actual data, security, and delivery constraints
- A high-value AI project must be finished in weeks without assembling and managing a temporary team
- Developers already use AI tools but cost, security, quality, and governance are unmanaged
- An experienced technical leader is needed while recruiting or reorganizing permanent leadership

## Selected use cases

- Requirements pipeline repair (lead case): advanced RAG index (Qdrant, dense + sparse vectors) over codebase and work-item history that analyzes and enriches Jira / Azure DevOps work items against actual code before development starts. The workflow also understands images — architecture diagrams, whiteboard sketches, UI design fragments — folding them into the same analysis. Delivered as consulting methodology on client infrastructure. Two engagements (3 and 4 weeks): developer-analyst communication reduced ~70%, feature rollout time cut ~30%.
- Multi-repository code intelligence: Qdrant-based RAG index plus a custom MCP server that lets AI coding agents (Claude Code, OpenAI Codex) retrieve precise code context across many repositories instead of exploring file trees — dramatically reducing token consumption and cost.

- PowerIntel — rebuilding software delivery around three AI-augmented senior engineers (from a 31-person outsourced team), using multi-model LLM routing to cut token costs and LLM-as-a-judge for automated quality gating. Result: full in-house code ownership, tighter security, faster delivery.


- RAG optimization studies: systematic model selection across every pipeline stage (embeddings, parametrized chunking, chunk enrichment, query rephrasing, generation) evaluated against a customer-specific gold dataset with recall metrics. Deliverable: a measured cost-versus-quality curve and a reusable evaluation harness, not a recommendation slide.

- Analytics platform modernization (utility sector): replaced hand-coded React dashboards with Apache Superset/Preset on Snowflake after a methodical evaluation (Power BI, Tableau, Omni, others). Multi-layer geospatial rendering of the utility network (nodes, branches, area groupings); dashboards became analyst-owned configuration instead of engineering backlog items. Snowflake Cortex integrated into the dashboards lets users ask questions of the data in plain English and receive governed answers — AI embedded in the tools people already use.

- Sensitive data discovery / AI readiness: AWS Macie and Microsoft Purview implementations that classify sensitive data (PII, PHI, payment data) across the data estate before it reaches AI pipelines. Governance perimeter first, AI architecture inside it. Experience aligned with ISO 27001, SOC 2, HIPAA, PCI-DSS 4.0.

## Further capabilities

- Enterprise BI at scale: Power BI / Microsoft Fabric (Spark, Lakehouses, Git-integrated pipelines, embedded reports, capacity optimization); AWS QuickSight with Snowflake and Athena for long-run cost control
- Applied AI beyond text: object detection and narrative generation from visual data; LLM fine-tuning; OpenAI and Copilot analytics integration
- Modern delivery: microservices and micro-frontends, Azure and AWS CI/CD, cloud cost optimization, secure SDLC (ISO 27001, SOC 2, HIPAA, PCI-DSS 4.0)

## Pages

- [Home](https://weboracles.com/): services, use cases, contact

## Contact

- Email: alecg@weboracles.com
- LinkedIn: https://www.linkedin.com/in/REPLACE-WITH-YOUR-LINKEDIN