Frequently data science solutions are slow to take hold. With the experience that PPDS brings to the table, we are able to rapidly bring value with novel tools deployed in production environments.
Extract structured intelligence from unstructured documents using statistical models, NLP, and agentic AI workflows.
Discover MoreBuild scalable, compliant data architectures that fuel trustworthy AI models and downstream operational pipelines.
Discover MoreEngineer production-grade models through fine-tuning, custom training, and scalable cloud-native MLOps deployments.
Discover MoreUse machine learning to predict system failures, optimize asset management, and reduce downtime across critical operations.
Discover MoreDeploy AI and data systems rapidly and securely through Infrastructure as Code, CI/CD, and modern DevSecOps practices.
Discover MoreNavigate the legal, ethical, and security risks associated with AI model selection, deployment, and operational usage.
Discover MoreAt Pikes Peak Data Sciences, we deliver end-to-end AI document processing platforms that transform raw documents into structured, actionable intelligence. Using a modular pipeline architecture, we automate the ingestion, parsing, classification, and semantic extraction of information trapped in unstructured formats — including clinical protocols, defense specifications, engineering documents, and policy manuals. Our solution architecture typically combines OCR, layout analysis, entity recognition (NER), relationship mapping, and context-aware summarization powered by large language models and advanced statistical methods. With agentic AI layers, our systems can dynamically adjust parsing strategies based on document complexity, type, or changing operational needs. Deployed through secure Infrastructure as Code (IaC) templates into AWS or Azure environments, our document processing products are fully auditable, version-controlled, and scalable. We dramatically reduce the human labor required for document review, compliance, and intelligence extraction — unlocking rapid decision-making, improved operational readiness, and significant cost savings for clients operating in defense, aerospace, clinical research, and government contracting.
At PPDS, we design and implement enterprise-grade data governance and engineering frameworks that ensure your AI initiatives are built on a foundation of trust, compliance, and operational integrity. We don't just move data — we build cloud-native data ecosystems where every data element is traceable, secure, and usable for mission-critical applications. Our product suite typically includes automated ingestion pipelines for structured and unstructured data, dynamic schema evolution, metadata management systems, access control enforcement, and policy-driven data lifecycle management — all delivered using Terraform, Airflow, and serverless orchestration frameworks. We specialize in environments where data must meet strict regulatory requirements (e.g., FedRAMP, HIPAA, DoD SRG). Our architectures support automatic compliance auditing, secure multi-tenant data lakes, lineage tracking, and ML-ready data curation. For our clients, this means faster time-to-insight, lower operational risk, and the ability to deploy AI systems with confidence that the underlying data assets meet legal, security, and mission-readiness standards.
Pikes Peak Data Sciences builds production-ready machine learning systems that solve real business and operational challenges. Our ML engineering practice focuses on transforming models into deployable, scalable, and governable software products — not just experiments. We design modular ML pipelines with automated data preprocessing, feature store integration, model training, hyperparameter optimization, evaluation, versioning, and drift detection — all orchestrated through cloud-native MLOps frameworks like SageMaker Pipelines, Azure ML, and Kubernetes-native ML toolchains. Beyond training models, we operationalize them: containerizing inference services, building real-time scoring endpoints, embedding models into agentic workflows, and integrating outputs into downstream business systems (BI tools, operational dashboards, automated decisioning engines). Whether we're fine-tuning an open-source large language model for regulatory compliance summarization or deploying predictive sustainment models for aerospace assets, our ML solutions are hardened for continuous delivery, monitoring, and adaptation.
Predictive sustainment solutions from PPDS allow organizations to transition from costly, reactive maintenance to proactive, predictive asset management — improving uptime, extending asset lifespans, and optimizing resource allocation. Our platform approach combines time-series sensor data, maintenance logs, operational context, and domain-specific knowledge into unified predictive models. Using survival analysis, anomaly detection, sequence modeling (LSTM, transformers), and probabilistic forecasting, we deliver predictive insights that drive smarter maintenance schedules and parts logistics. All predictive maintenance models are wrapped in cloud-native deployment pipelines with real-time monitoring, retraining triggers, alerting integrations, and operational dashboards. We also embed feedback loops so that field technician reports or post-event analyses continuously refine model performance over time. Clients leveraging our predictive maintenance products see measurable improvements in operational availability, cost savings through optimized inventory management, and enhanced mission assurance — particularly in high-consequence domains like aerospace, defense, and energy.
At Pikes Peak Data Sciences, cloud engineering is not an afterthought — it is a primary enabler of scalable, secure AI and data solutions. We build cloud-native architectures that use Infrastructure as Code (Terraform, Pulumi), CI/CD pipelines (GitLab, GitHub Actions, Azure DevOps), container orchestration (ECS, AKS), and DevSecOps best practices to automate deployments, security, and compliance. Our cloud solutions are designed to be modular, auditable, reproducible, and portable — whether you are operating in AWS GovCloud, Azure Government, hybrid, or edge environments. We provide hardened landing zones for AI workloads, secure VPC architectures, automated policy enforcement, and cost-optimized resource scaling. For clients, this means faster provisioning cycles, lower risk of misconfiguration or drift, full compliance traceability, and architectures that can scale elastically based on mission need. It also means deploying AI products not in months, but in days or weeks, accelerating your return on AI investments.
Ethical and secure AI deployment is at the core of Pikes Peak Data Sciences’ value proposition. In a world where AI models are increasingly making consequential decisions, we help clients implement practices that ensure fairness, transparency, security, and accountability. Our solutions include model sourcing audits (to verify data and license provenance), bias and fairness evaluations, adversarial robustness testing, explainable AI (XAI) frameworks, and secure model deployment pipelines. We also design risk-based access controls and monitoring systems that detect data drift, performance degradation, or anomalous model behavior post-deployment. PPDS assists government, defense, and enterprise clients in integrating AI risk management frameworks into their broader operational governance structures — ensuring that AI-driven systems are not only technically excellent but legally defensible and ethically resilient.
We combine expertise in Agentic AI, Machine Learning, Data Engineering, and Cloud Infrastructure to build solutions that are scalable, reliable, and operational from Day 1.
We build AI systems that act as autonomous agents — gathering information, making decisions, and adapting workflows in real time. Our Agentic AI frameworks integrate large language models, orchestration tools, and policy layers to move beyond basic automation into intelligent, mission-driven execution.
Our ML engineers design, fine-tune, and deploy models that are tailored for operational use cases — from predictive sustainment to document understanding. We build repeatable, scalable MLOps pipelines that accelerate experimentation without sacrificing security, governance, or production readiness.
Machine learning success starts with robust, trusted data. Our data engineering teams build ingestion pipelines, document processing flows, and curated datasets — ensuring that your AI systems are fueled by clean, structured, and operational-grade information.
We deploy AI solutions with cloud-native best practices and Infrastructure as Code (IaC), enabling secure, auditable, and scalable environments. From AWS to Azure, our systems are built to move from prototype to production with speed, reliability, and compliance.
At Pikes Peak Data Sciences, we deliver operational AI and data solutions that solve real business challenges across industries like clinical research, aerospace, defense, and government contracting. Here’s how we’ve turned complex problems into successful products and platforms.
Problem: Evaluating clinical trial protocols for risk required intense physician review of documents exceeding 300 pages.
Solution: Delivered a working POC in two weeks that automatically evaluates, suggests, and reasons about protocol risks using LLMs, Agentic Frameworks, and Chain of Thought Reasoning deployed on Azure. Approved for production deployment by JP Morgan leadership.
Problem: Manual QA of pre-launch simulation data was slow, labor-intensive, and error-prone.
Solution: Built an anomaly detection pipeline utilizing Mixture of Experts models, Autoencoders, and AWS cloud-native services, highlighting critical anomalies for targeted review.
Additional Problem: Siloed simulation data was inaccessible to automated systems.
Solution: Developed a secure, scalable Data Lakehouse solution powered by AWS Athena, Glue, ECS, Lambda, Airflow, Grafana, Agentic AI, and LLMs — enabling automated insight generation and DevSecOps-aligned deployments.
Problem: Difficulties in leveraging past performance data to uncover new government contracting opportunities.
Solution: Adapted PPDS’s proprietary SAMML platform to scrape past performance records and apply LLM-based classification models, creating an AWS-native solution for opportunity generation.
Problem: Challenges in efficiently evaluating SBIR submissions for compliance and matching them to qualified judges.
Solution: Designed and deployed an intelligent matching system using NLP pipelines, graph databases, and Retrieval-Augmented Generation (RAG) techniques to streamline evaluation.
Problem: Need for an internal, self-hosted LLM solution fine-tuned on proprietary company data.
Solution: Currently engaged in fine-tuning a secure, private LLM deployment to power Odyssey’s agentic AI initiatives, ensuring control over intellectual property and operational independence.
Problem: EEG processing workflows were time-consuming and lacked cloud integration for scalability.
Solution: Engineered a custom data pipeline integrating APIs, Google Drive automation, and a lightweight local UI, enabling streamlined EEG processing without cloud dependencies while maintaining HIPAA compliance.