Key Takeaways

• Infrastructure gaps, not model quality, cause 70-87% of AI projects to fail before production.
• Four pillars determine production readiness: integration points, data flow architecture, compliance frameworks, and measurement systems. 
• Compliance built in from the beginning accelerates deployment, not slows it. 
• Model monitoring requires tracking data drift, concept drift, and business outcomes beyond traditional APM metrics. 
• Parallel-run migration delivers infrastructure modernization with zero downtime in 8-12 weeks. 

The people who get stuck with AI in production usually do not have a model problem. They have an infrastructure problem.

Leadership has already approved the GenAI initiative. The prototype works in the demo. The team can show clever prompt engineering and good offline metrics. Then someone asks a simple question: “How does this plug into everything we already run in production?”

At that moment the real work starts. There are Snowflake connections to respect, SOC 2 or HIPAA rules to meet, 500K daily transactions to handle, and existing reporting pipelines that cannot break. That is where most projects slow down or stall. 

Surveys across industries report that between 70% and 87% of AI and ML projects never make it into production environments. Analysts also estimate billions of dollars in wasted AI spend each year when pilots never reach customers.

This is the gap where Torsion operates. Torsion focuses on the infrastructure that makes models deployable and reliable rather than on marginal gains in benchmark accuracy. This blog walks through four pillars that determine whether your AI project survives that gap: integration points, data flow architecture, compliance frameworks, and measurement systems.

The Invisible Work That Makes AI Real

Why Model Metrics Are Not Enough

Most teams spend their early cycles on model accuracy, latency, and prompts. Those are important. They are also not the reasons most projects die. Reviews of failed enterprise AI initiatives repeatedly show that infrastructure issues cause the majority of breakdowns. Common causes include data quality, system integration problems, and the lack of reliable pipelines and monitoring.

Several independent analyses estimate that well over two thirds of AI projects fail to move beyond experimentation. One often cited estimate from industry surveys puts the failure rate near 80%, with 87% of data science projects never making it into production workflows. In many of those cases the model works, but the surrounding systems do not.

Torsion’s work reflects the same pattern. When clients arrive, they usually have a prototype already running in a notebook or a lab environment. The missing piece is the surrounding infrastructure that makes that model safe, observable, compliant, and integrated with the rest of the stack.

Pillar 1: Integration Points That Touch The Real World

How Your Model Actually Meets Users

The first pillar covers how your model connects to real users and systems. In practice that means APIs, services, and the glue around your model. Torsion’s consulting offers are built around this point. The team designs integration architectures that define how LLMs and other models connect to production databases, third party services, and existing applications in a way that operations teams can support.

At a basic level there are a few common integration patterns.

Torsion generally fronts these patterns with an API gateway. The gateway handles concerns such as rate limiting, authentication, request validation, and routing. That matches modern guidance from cloud providers and infrastructure teams using tools like AWS API Gateway, Kong, or Apigee. It also creates a clean boundary between caller and model, which matters when you later add more services or change model backends.

Choosing Model Serving Frameworks

Under the API layer you need model serving infrastructure. For LLMs and multi model systems, most modern stacks use one or more specialized frameworks.

• TensorFlow Serving and TorchServe work well for single framework models, such as pure TensorFlow or PyTorch deployments.
• NVIDIA Triton Inference Server can serve models from multiple frameworks and supports batching and ensembles in production. It fits environments with mixed workloads such as vision, embeddings, and classical ML.
• vLLM is tuned for LLM workloads. It optimizes memory usage and time to first token using techniques such as PagedAttention. It can stream token outputs efficiently and serves high throughput LLM traffic.

For many production LLM systems, teams end up using both vLLM and Triton. vLLM handles the main generative model while Triton serves embeddings, rerankers, or other non LLM models. Torsion works within these established patterns rather than trying to lock clients into a proprietary serving runtime. Its focus is to pick an appropriate combination that fits the client’s stack, performance needs, and operations capabilities.

Integrating With Data Sources Safely

Integration also covers how models see data. Most production systems mix batch and streaming information.

• Batch data from warehouses such as Snowflake, BigQuery, or Databricks for periodic scoring jobs and training.
• Streaming data from Kafka, Kinesis, or similar tools for real time inference or event driven triggers.
• Feature stores that hold precomputed features for both training and serving so that the model sees consistent inputs in both phases.

Industry guidance is clear that feature stores reduce training serving skew and provide a consistent interface for models. They typically include an offline store for training data, an online low latency store for live calls, and a registry that tracks feature definitions and versions.

Torsion’s data engineering services cover this integration layer. They build the pipelines that move data from upstream systems into forms that models can consume and maintain, with appropriate transformation and validation steps.

Keeping Integration Secure

Every integration point is a potential security risk. For AI systems that touch sensitive data, this becomes central rather than optional. 

Torsion’s enterprise deployment work packages these patterns into reference architectures and infrastructure as code so that security is not left as an afterthought. That matters in regulated environments such as healthcare where HIPAA requires specific safeguards around access logging, encryption, and business associate agreements.

Pillar 2: Data Flow Architecture That Feeds Your Models

How Data Moves Through The System

The second pillar covers how data moves through your environment. Many AI projects fail because the data paths are brittle or opaque. Studies of AI proof of concept failures report that data preparation alone often consumes 60% to 70% of project timelines, and that data quality issues are a leading cause of early proof of concept failure.

Research on ETL and ELT for machine learning shows that ELT on cloud warehouses can improve processing efficiency, flexibility, and iteration speed compared to traditional ETL, especially when workloads and transformations change frequently.

Torsion’s legacy data infrastructure work involves moving clients from older ETL setups to modern pipelines on cloud platforms. For one healthcare analytics platform Torsion reverse engineered 15 year old Perl scripts, rebuilt the pipelines using Python and Airflow, and validated in parallel before cutover. Processing time dropped from many hours to under an hour without downtime.

Orchestration And Reliability

Data flow needs orchestration rather than ad hoc scripts. The ecosystem offers several mature options.

• Apache Airflow orchestrates batch and workflow style pipelines and is widely used in data engineering.
• Kubeflow targets machine learning workflows on Kubernetes including training, tuning, and deployment.
• Prefect and Dagster provide more modern takes on orchestration with strong Python support and improved developer ergonomics.

Each tool has strengths and trade offs. Airflow has a large ecosystem and is familiar to many data engineers, but can be complex to scale. Kubeflow offers deep Kubernetes integration but assumes strong operational expertise. Prefect and similar tools aim to simplify orchestration with less overhead for teams that prefer a lighter control plane.

Torsion selects orchestration based on client context instead of forcing a single technology. In migrations documented in its materials, Airflow is often used because it integrates well with both legacy systems and modern cloud data platforms while remaining accessible to existing teams.

Storage: Lakes, Warehouses, Features, Vectors

Underneath orchestration you need aligned storage layers.

• Data lakes in object storage such as S3 or ADLS hold raw and semi structured data at scale, which is useful for future training and backfills.
• Data warehouses like Snowflake, BigQuery, Redshift, or Databricks power analytical queries and can host transformation logic for ELT.
• Feature stores sit between raw data and models and provide consistent feature definitions and access paths for both training and serving.
• Vector databases store embeddings and power similarity search for retrieval augmented generation, semantic search, and recommendation systems.

Torsion’s healthcare and analytics case studies show full stacks that combine S3 based data lakes, cataloging and query layers such as Glue and Athena, and then higher level pieces for model workloads. This structure keeps raw data, transformed data, and model inputs organized in a way that can be governed, audited, and scaled.

Pillar 3: Compliance Frameworks That Keep You Out Of Trouble

Why Governance Accelerates, Not Slows, AI

The third pillar deals with compliance and governance. Many engineering leaders think of compliance as friction, but in practice clear frameworks reduce risk and help projects move faster through internal review. 

Several major standards show up repeatedly in AI work.

• HIPAA governs protected health information in healthcare in the United States and sets requirements around access controls, audit logging, and safeguards such as encryption.
• GDPR in the European Union covers personal data and includes concepts such as data minimization, purpose limitation, and rights around automated decision making.
• SOC 2 defines criteria for security, availability, processing integrity, confidentiality, and privacy in service organizations and is often requested by enterprise customers.

Torsion explicitly supports HIPAA, SOC 2, and GDPR in its data migration and AI deployment work. Its materials describe security designs that include encryption at rest and in transit, role based access control, audit logging, and documented data lineage, which are all recurring requirements in those frameworks.

Privacy By Design In AI Systems

Privacy by design is a concept that originated in data protection and has been incorporated into GDPR and other regulations. The core idea is that privacy should be embedded into systems from the beginning, rather than added later. Authorities and practitioners often describe seven foundational principles, including proactive design, privacy as default, full functionality, end to end security, visibility, and respect for user privacy.

In AI systems that means:

• Minimizing which data fields models see in both training and inference.
• De identifying or tokenizing sensitive identifiers when possible and managing the mapping securely.
• Maintaining clear records of where training data came from and how it is used.
• Designing APIs that expose only the minimum information necessary.
• Providing documentation and where possible explanations about how predictions are made, particularly when they affect individuals.

Torsion implements these ideas concretely in projects such as HIPAA compliant data lakes and AI workloads. In a regional payer engagement, Torsion implemented de-identification pipelines, strong IAM policies, and audit logging before enabling LLM analytics workloads. The client then passed a HIPAA audit and could trace how member data flowed into and through the system.

Compliance Automation Tools

Many teams adopt compliance automation platforms to reduce manual effort. Tools such as Vanta, Drata, and Scytale connect to cloud providers, version control, and HR systems to collect evidence and track controls for standards such as SOC 2, HIPAA, and GDPR. These platforms typically:

• Monitor configuration drift and alert on violations.
• Maintain policy documents and training records.
• Provide dashboards and reports for auditors.
• Help manage vendor risk evaluations.

Torsion does not build these platforms, but its architectures integrate with them. For example, using infrastructure as code makes it easier to satisfy configuration evidence requests, while centralized logging supports log retention and investigation requirements. When Torsion designs a data or AI platform, it does so with the assumption that compliance automation and audits will run against it.

Pillar 4: Measurement Systems That Tell You When AI Works

Why Observability Matters For Models

The fourth pillar covers measurement and observability. Traditional application monitoring focuses on CPU, memory, request latency, and error rates. Those metrics still matter for AI services, but they are not enough. A model can respond quickly and without errors while giving poor answers.

Recent guides on ML observability highlight the need to treat models as dynamic systems that change over time as environments, data, and user behavior shift. They recommend tracking feature distributions, prediction distributions, and performance metrics and comparing them to baselines from training or previous stable periods.

Torsion’s optimization and governance services align directly with this. They focus on monitoring, retraining pipelines, and continuous improvement rather than one time model deployment.Torsion-Brand-Narrative.pdf

Detecting Drift And Knowing When To Retrain

Model drift shows up in several forms.

• Data drift occurs when the statistical properties of input features shift relative to training distributions.
• Concept drift happens when the relationship between inputs and outputs changes, such as a fraud model that becomes less effective after criminals adapt.
• Prediction drift refers to changes in the distribution of model outputs over time.

Common ways to detect drift include:

• Statistical tests such as Population Stability Index and Kolmogorov Smirnov for feature distributions.
• Monitoring performance metrics against holdout labels where those are available.
• Watching for correlated changes between model metrics and business outcomes.

Guides on retraining strategies suggest three broad approaches: schedule based retraining at fixed intervals, trigger based retraining when metrics cross thresholds, and hybrid strategies that combine both. The right choice depends on domain volatility and the cost of stale models.

Torsion’s Business Metrics and ROI instrumentation work explicitly ties technical metrics to business impact, which is key to setting meaningful retraining triggers. For example, Torsion has built ROI frameworks that map time saved by an automated reporting system into dollar savings and then uses those metrics to track whether systems are living up to their expected value.

Building A Monitoring Architecture

A practical monitoring stack for AI usually has these layers:

• Collection of inputs, outputs, and metadata, often via logging or specialized SDKs.
• Storage in time series databases and warehouses for analysis.
• Processing jobs that calculate drift scores, performance metrics, and aggregates.
• Dashboards and alerts in tools such as Grafana, Datadog, or specialized ML observability platforms.
• Automated actions that can trigger retraining pipelines or reduce traffic to degraded models.

Torsion leans on existing tools rather than reinventing monitoring. In many organizations that means integrating with existing observability stacks while adding model specific metrics and dashboards. This keeps operations teams in familiar tools and adds an AI lens on top.

How The Four Pillars Work Together

A System, Not Separate Checklists

These four pillars do not exist in isolation. Integration points sit on top of data flows, compliance requirements influence how data is stored and accessed, while measurement systems watch all of the above.

The experience from Torsion projects shows that starting with data and integration, then layering compliance and measurement, produces a stable base. For example, in a healthcare analytics migration, Torsion first stabilized and modernized ETL pipelines, then added a HIPAA compliant data lake with strong access controls, and only then enabled LLM analytics workloads. Monitoring and audit logging were built in from the beginning rather than tacked on later.

The result was not only better performance and lower cost, but also easier audits and clearer insight into how data and models behaved in production. That same approach generalizes to other industries such as insurance and SaaS where the regulatory frameworks differ but the need for clear infrastructure remains.

What This Means For You As An Engineering Leader

If you lead engineering, data, or AI teams, you sit exactly where these decisions converge. You are accountable to executives for results, to your team for technical choices, and to regulators or customers for safety and reliability. Surveys of CTOs and VP Engineering roles show that the top AI challenges they report are not about algorithm choice but about integration, data management, and deployment risk.

The practical implications are clear.

• Time spent on infrastructure is not a distraction from AI, it is what makes AI real.
• Focusing only on model metrics while ignoring pipelines, governance, and monitoring keeps projects stuck in prototypes.
• Investing in infrastructure early reduces the likelihood that you join the majority of organizations whose AI projects never reach production.

Torsion’s work is structured around that reality. Its services span discovery and strategy, proof of concept development, enterprise deployment and scaling, and optimization and governance, with a consistent emphasis on working infrastructure and lifecycle coverage. For teams who already have models that work in a demo, the value is in making those models part of systems that your organization can run, trust, and evolve.