Mcknight Consulting Group / SERVICES

We do not rely on vendor claims; instead, we execute industry-standard specifications like TPC-C (transactional), TPC-H, and TPC-DS (analytical) across multi-terabyte datasets (e.g., 10TB to 100TB). We also utilize custom YCSB (Yahoo! Cloud Serving Benchmark) workloads to stress-test NoSQL platforms.

We measure deep technical metrics such as P50, P90, and P99 read/write latencies, throughput (operations per second), and geometrical mean performance. For vector databases, we measure active indexing speeds, HNSW (Hierarchical Navigable Small World) index recall accuracy, and F1 scores, exposing systems that suffer from unacceptably low variance (e.g., dropping to 23% recall during ingestion) versus those maintaining 90%+ accuracy. We then synthesize these metrics into a highly deterministic "price-performance" ratio (dollars per query hour).

We architect systems that decouple the compute engine from the storage layer using Open Table Formats like Apache Iceberg, Apache Hudi, and Delta Lake, pushing at the file level for columnar formats like Apache Parquet and Avro that utilize advanced compression techniques to dramatically reduce I/O overhead.

This allows operational data to land directly in low-cost object storage, enabling different compute engines to execute SQL and ML workloads against a single source of truth without the architectural tax of data movement or vendor lock-in, while also deploying Hybrid Transactional/Analytical Processing (HTAP) systems that seamlessly sync row-based and columnar storage to blur the lines between OLTP and OLAP.

We deploy unsupervised machine learning models directly onto the data stream. These tools utilize predictive time-series analysis to learn the historical behavioral baseline of pipelines and detect statistically significant deviations like data drift, volume volatility, and schema changes in real time.

Instead of manually sifting through logs, our ML-driven approach acts as an automated circuit breaker—halting data pipelines and providing root cause and impact analysis before corrupted data can poison downstream machine learning algorithms or Agentic AI systems.

We integrate AI directly into the MDM workflow by leveraging Natural Language Processing and Named Entity Recognition to autonomously extract hidden attributes from unstructured text to feed the MDM hub, alongside Large Language Models to automatically generate descriptions and categorize data.

This creates a centralized, highly-governed "golden record" that acts as the contextual grounding layer for Retrieval-Augmented Generation applications, often interacting with AI agents directly via the Model Context Protocol.

We navigate the complex engineering trade-offs of streaming, such as configuring at-least-once versus exactly-once delivery semantics and guaranteeing order of delivery, while implementing software-defined, multi-modal streaming engines that allow engineers to precisely tune the balance between speed and storage at the individual topic level.

This streaming architecture provides the real-time situational awareness required for event-driven Agentic AI applications, allowing them to autonomously detect and react to supply chain or fraud anomalies within milliseconds.

We evaluate and implement vector databases by testing specific embedding models and indexing methods like HNSW, measuring the exact performance of K-Nearest Neighbor algorithms using Euclidean and cosine distance functions to navigate high-dimensional space.

For complex relationship mapping, we implement RDF semantic graphs utilizing subject-predicate-object triples, deploying advanced algorithms like PageRank to determine node influence and the Louvain method for entity resolution to quantify modularity scores and detect dense communities in large networks.

We architect "Zero DBA" embedded databases designed to run on resource-constrained devices while utilizing specialized edge AI chips for local model inference, implementing HTAP at the edge to maintain synchronized row-based and columnar storage for simultaneous lightweight OLAP queries and transactions.

We handle append-only, time-ordered time-series data using optimization techniques like downsampling to reduce data points while preserving historical change trends, carefully tuning the push/pull synchronization latency between the edge nodes and the central cloud.

We connect autonomous agents to structured corporate data via the Model Context Protocol, building a rigorous Knowledge Graph semantic layer that injects precise metadata, business logic, governance policies, and data lineage directly into the agent's context window.

This eliminates the context bottleneck, evolving analytics from passive dashboards into an event-driven architecture where AI agents can proactively spot anomalies and trigger automated workflow remediation.

We model project returns using probability distributions—calculating best-case, plan-case, and worst-case scenarios based on specific percentages—and utilize advanced financial metrics like Net Present Value, Internal Rate of Return, and discount hurdle rates.

To accurately forecast Total Cost of Ownership, we treat architecture builds as agile projects, mapping out every technical task into T-shirt sizes and story points using a modified Fibonacci sequence, which is then divided by team velocity to calculate the exact Full-Time Equivalent labor costs for both the initial build and steady-state operations.

We architect individual spokes on the mesh that curate specific subject areas, establishing rigid, API-driven Data Contracts between domains instead of ad-hoc ETL scripts.

This treats data as an internal, highly available product with strict SLAs, combining this mesh architecture with Open Table Formats and zero-ETL federation so data scientists and AI models can instantly query across domains without physically moving or copying the underlying files.