We help U.S. enterprises turn models into measurable outcomes by combining platform thinking, product focus, and a thin-slice MVP approach that proves value quickly and scales. Our consulting-led model aligns objectives, identifies data sources, and defines a clear roadmap from build-test-deploy to monitor, backed by NDA-protected conversations.
We automate pipelines, leverage AutoML, and deploy on AWS, Azure, and Google Cloud to deliver high availability and secure, vendor-agnostic architectures. We focus on continuous proof-of-value, implementing distributed tracing, log analysis, and anomaly detection so leaders can track performance, cost, and time-to-value with transparent dashboards.
Our services reduce operational burden, standardize workflows with CI/CD and observability, and provide ongoing optimization with retraining triggers and proactive monitoring. We integrate with client teams to transfer knowledge and ensure lasting success as data and markets evolve.
Key Takeaways
- We streamline the machine learning lifecycle end-to-end, aligning technical work with business goals.
- Thin-slice MVPs validate use cases fast and create a path to scalable solutions.
- Vendor-agnostic, cloud-first deployments on AWS, Azure, and Google Cloud ensure reliability and scale.
- Continuous instrumentation gives leaders clear metrics for performance, cost, and time-to-value.
- Security, governance, and ongoing optimization keep models reliable as conditions change.
Why Choose an MLOps Partner India for U.S. Enterprises
We translate business goals into practical, measurable ML roadmaps, pairing strategic consulting with hands-on delivery to move pilots into production quickly.
Our cross-functional team blends data engineering, development, and operations so you get fewer handoffs and faster releases. We bring deep cloud experience across leading platforms and design resilient infrastructure that fits your risk and cost profile.
Security and compliance are baked in from day one, and our vendor-agnostic approach lets us recommend the best tools for your environment. We run automated pipelines, versioning, testing, and rollback patterns to minimize downtime and detect drift early.
- Round-the-clock collaboration for continuous progress and cost-efficient access to specialists.
- Consulting-first delivery that ties technical work to business impact and clear metrics.
- Knowledge transfer and enablement so your teams operate the platform long term.
| Value | What We Deliver | Cloud Support | Outcome |
|---|---|---|---|
| Strategy | Objectives, roadmaps, prioritized goals | AWS, Azure, Google Cloud | Faster time to value |
| Engineering | Pipelines, CI/CD, model registry | Cloud-native services and hybrid patterns | Stable production releases |
| Governance | Security, auditability, compliance frameworks | Role-based controls and encryption | Enterprise-grade trust |
Business Impact at Present: Faster Time to Value and Lower Ops Overhead
We compress the path from idea to impact by using focused, product-minded MVPs and platform patterns that prove value quickly, reduce risk, and free teams to work on higher-value problems.
Accelerate time to value by up to 5x with thin-slice MVPs
Our thin-slice MVPs validate hypotheses in weeks, not months, letting leadership decide fast and expand incrementally with lower exposure. This pattern amplifies conversion and performance signals early, so stakeholders see tangible returns sooner.
Boost operations efficiency by up to 20% via value stream mapping
Value stream mapping uncovers delays and handoffs across the ML process, enabling targeted automation and standardization that lift overall efficiency.
We automate pipelines from ingestion to deployment, ensure reproducibility across environments, and integrate CI/CD so experiments iterate faster with fewer errors.
- Quantified upside: up to 5x faster time to value and ~20% efficiency gains.
- Continuous monitoring: proof-of-value dashboards track conversion uplift, risk reduction, and cost drivers.
- Operational clarity: observability provides actionable insights into latency, throughput, and infrastructure spend.
These improvements de-risk portfolios, accelerate learning, and increase the probability of business success, combining technical rigor with practical governance so teams deliver repeatable, cost-controlled solutions backed by our expertise.
End-to-End MLOps Services That Power Your ML Lifecycle
We provide comprehensive services that connect discovery to live operations, so models graduate quickly from prototype to measurable impact.
From development and training to deployment and production
Our team covers the full lifecycle, including discovery, data preparation, model development, training, validation, and production operations.
Automated pipelines orchestrate data ingestion, feature engineering, and training with reusable templates to ensure consistent delivery and faster iteration.
We build CI/CD for ML that adds testing gates for data and model quality, so deployments are repeatable and safe with minimal manual steps.
Production readiness includes model packaging, containerization, registries, and promotion workflows that simplify staging-to-live moves.
- Cloud-native infrastructure on AWS, Azure, and Google Cloud for high availability and autoscaling.
- Integrated observability with metrics, logs, traces, and drift detection to shorten time-to-detect.
- Lifecycle governance with access control, audit logs, and compliance workflows that meet enterprise needs.
We remain tool-agnostic, integrating best-fit tools and frameworks so your teams keep momentum while we tie each phase to clear KPIs and business outcomes.
MLOps Partner India
We deliver practical solutions that turn models into reliable services, covering model development, testing, deployment, and continuous monitoring.
Our multidisciplinary team brings deep expertise across data engineering, platform engineering, and model development, so work moves smoothly from ideation to production.
We offer modular services—pipeline design, CI/CD for model delivery, versioning and registry, monitoring, and governance—that adapt to each client’s stack.
- Flexible environments: on-premise, cloud, or hybrid deployments that integrate with existing systems and compliance workflows.
- Safe releases: continuous delivery with rollback safety nets and incremental updates to reduce deployment risk.
- Live operations: real-time monitoring and automated retraining pipelines that detect drift and trigger remediation.
Our consulting-first approach clarifies objectives, prioritizes quick wins, and aligns technical work to business outcomes.
We commit to ongoing support and optimization, combining field experience with transparent metrics to secure stakeholder buy-in and drive measurable success. Contact us to map program goals and a pragmatic roadmap to scale.
Thin-Slice Approach and Product Thinking for Rapid MVPs
We drive rapid learning by shipping the smallest viable capability that ties directly to business metrics, delivering a clear roadmap from build to monitor while reducing upfront risk.
Our thin-slice approach delivers the smallest end-to-end slice that proves value, enabling quick feedback loops and fast adjustments before wider development.
We pair product thinking with platform skills to focus releases on user needs and measurable outcomes. This keeps goals aligned and stakeholders confident as the program scales.
- We identify high-impact opportunities, map dependencies, and plan a delivery path that prioritizes observable results over big-bang releases.
- MVPs are instrumented with analytics and continuous proof-of-value metrics to capture adoption, accuracy, and cost insights that guide next steps.
- Workflows are standardized with templates and checklists so speed does not sacrifice data quality, testing, or safe deployment.
| Feature | Thin-Slice | Traditional | Scalable Path |
|---|---|---|---|
| Scope | Minimal end-to-end slice | Large, phased project | Incremental portfolio build |
| Risk | Low – fast feedback | High – long validation | Managed – gates and rollouts |
| Measurement | Real adoption & cost metrics | Benchmark or pilot reports | Continuous proof-of-value |
| Platform | Cloud-native, portable | Platform-tied, heavy lift | Vendor-agnostic templates |
The result: faster value realization, clearer insights for decision gates, and architectures that scale from one use case to many without rework. This approach shortens development cycles and aligns the process to measurable business goals while keeping cloud portability in mind.
Automated ML Pipelines: From Data Processing to Model Training
We build end-to-end automation that turns raw datasets into repeatable training runs with minimal manual steps. Our pipelines enforce consistent stages—ingestion, validation, feature engineering, and training—so every run produces the same artifacts and metadata.
AutoML enablement standardizes experiments, producing reproducible baselines and measurable comparisons that accelerate candidate selection and reduce bias from ad-hoc development.
CI-ready components for faster iteration cycles
We deliver reusable preprocessing modules, containerized training, and parameterized workflows that support hyperparameter sweeps and parallel experiments, shortening time to find high-performance models.
- Data validation at pipeline entry points guards model quality and reduces runtime failures.
- Lineage and metadata tools capture versions, datasets, and settings for each training run.
- Testing gates enforce performance and quality thresholds before promotion to staging or production.
Centralized pipeline templates reduce variability across teams, cut onboarding time, and free data scientists from repetitive tasks so they focus on feature design and model innovation. These practices raise both speed and reliability, aligning delivery to business timelines and service-level expectations.
CI/CD for Machine Learning: Testing, Validation, and Model Deployment
Continuous delivery for ML ties data checks, metric thresholds, and deployment patterns to minimize surprises in production. Our CI/CD workflows automate testing and deployment so teams iterate quickly while keeping strict control over risk and traceability.
Automated testing gates for data and model quality
We validate schemas, monitor feature distributions, and run metric checks before any promotion. Gates block promotions when accuracy, AUC, latency, or fairness fall below policy targets.
Blue/green and canary strategies with rollback
Phased rollouts let new models serve a subset of traffic while health checks run. If anomalies appear, we trigger safe rollback paths to the prior version, reducing downtime and user impact.
Model registry and production-ready repositories
Our model registry records lineage, signatures, and artifacts with approval workflows that support auditability. Production repositories store packaged models and containers with immutable tags to simplify version control and repeatable deployments.
- Integrate with infrastructure-as-code and policy-as-code for environment parity and compliance.
- Support real-time, batch, and streaming inference patterns to match business workflows.
- Standardized pipelines scale from single models to an enterprise portfolio with less operational overhead.
Cloud-Native Deployments on AWS, Azure, and Google Cloud
We design cloud-native reference architectures that use managed services to cut operational toil while boosting uptime and resilience.
Our architectures map to each major cloud and their native services, so deployments meet strict SLA targets without heavy maintenance. We pick the right platform components to match workload patterns, compliance needs, and data locality requirements.
High availability, scalability, and reliability under real-world loads
We implement multi-AZ and multi-region topologies, automated autoscaling, and failover routes to keep models serving during traffic spikes. These patterns reduce single points of failure and maintain consistent performance.
Operational controls include observability, cost quotas, and trace logs so teams monitor latency, throughput, and spend against KPIs. We integrate identity, secret management, and network segmentation to enforce least-privilege access.
- Reference designs that favor managed services to lower infrastructure overhead.
- Deployment patterns for online inference, batch scoring, and streaming analytics tuned per platform.
- Portability via abstraction layers and open standards to avoid vendor lock-in.
- Automation and tooling that make operations repeatable, auditable, and faster to recover.
The result: cloud solutions that balance speed, resilience, and cost, so your models deliver reliable business value in production.
Observability and Monitoring: Tracing, Logs, and Anomaly Detection
Real-time monitoring converts raw logs and traces into actionable alerts and retraining triggers for production models. We combine distributed tracing, structured log analysis, and anomaly detection so teams gain clear operational insights fast.
Distributed tracing and log analysis for ML workflows
We map end-to-end workflows so training, serving, and data pipelines are correlated, letting us isolate latency and failures quickly. Logs are parsed into structured events, enabling searchable insights and compliance-ready records.
Concept drift and data skew detection in real time
Automated monitors compare live distributions to training baselines and flag deviations when thresholds are exceeded. Alerts trigger investigations or automated retraining policies to protect model quality.
Automated performance capture and alerting
Dashboards track accuracy, precision/recall, calibration, p95 latency, and throughput, so stakeholders see performance at a glance. We integrate alerting with paging and incident tools and ship runbooks that guide consistent triage and resolution.
- Outcome: lower mean time to detect and resolve, sustained production reliability.
- Benefits: instrumented workflows, cost and scaling insights, and automated retraining that keeps models effective.
Governance, Security, and Compliance Across the ML Lifecycle
Across the model lifecycle we build traceable controls that ensure accountability, reduce risk, and speed approvals. We combine policy, engineering, and operational routines so regulated teams can move faster without sacrificing safety.
Data and model governance with auditability
We define governance policies for datasets, features, and models so lineage, ownership, approvals, and audit trails are recorded end-to-end.
We maintain documentation, datasheets, and monitoring artifacts to support reviews and audits.
Security in use, in transit, and at rest
We apply encryption standards for data at rest and in transit, and use protected enclaves or secure compute for data in use when needed.
Access control follows least-privilege principles with role-based schemes and secrets management across environments.
- Compliance-aligned workflows that enforce segregation of duties and documented sign-offs.
- Model risk management: validation, challenger models, and periodic reviews to protect quality and business outcomes.
- Automated policy checks and compliance-as-code in pipelines to keep operations fast and safe.
Our expertise in governance frameworks maps controls to industry standards so teams meet regulatory needs without heavy process overhead. Clear controls improve reliability, cut incidents, and make approvals repeatable.
Version Control, Experiment Tracking, and Model Registry
Version control ties code, configuration, and dataset references into a single, auditable source of truth that supports repeatable development cycles.
We capture experiments automatically, storing hyperparameters, metrics, artifacts, and environment details in a centralized metadata store so teams compare runs reliably.
Our process uses a model registry to manage states—staging, production, and archived—with approvals and changelogs that support safe promotion and rollback.
- Best practices for versioning ensure code, configs, and dataset pointers are tracked for reproducibility and auditability.
- Experiment tracking captures metrics and artifacts, enabling rigorous testing and informed development choices.
- A metadata store centralizes lineage and annotations so teams analyze trends and speed incident response.
We integrate common tools to automate these workflows, reduce manual steps, and maintain full traceability from idea to release.
Our expertise helps you pick the right mix of tools and controls so disciplined versioning and registries scale models safely across the enterprise.
Data Foundations: Validation, Feature Stores, and Reproducibility
Strong data foundations make model delivery predictable and auditable, so teams move from experimental proofs to reliable services with less rework.
We design acquisition programs, automated checks, and central feature governance that preserve point-in-time correctness and provenance. These practices shorten feedback loops and raise trust across stakeholders.
Automated data validation and splitting
Automated validation enforces schema checks, range constraints, missing-value policies, and drift detection at ingestion to catch issues early.
We automate data splitting to keep distributional parity and to prevent leakage, ensuring training and evaluation reflect real-world behavior, and supporting repeatable experiments.
Centralized feature store for consistency
A central feature store provides versioned features, point-in-time joins, and reuse across training and serving so models behave consistently in production.
Cataloging and lineage document sources, transformations, and approvals, giving teams transparent records for audits and faster incident analysis.
- Standardized processing and orchestration reduce variance across teams and make pipelines portable.
- Reproducibility practices—seed management, environment pinning, and artifact storage—enable consistent reruns and clean audits.
- Continuous monitoring flags anomalies that affect learning or user experience and triggers retraining or investigation.
| Capability | What We Deliver | Benefits |
|---|---|---|
| Validation | Schema, range, missing-value, drift checks | Higher upstream quality, fewer failures downstream |
| Feature Store | Versioning, point-in-time correctness, serving APIs | Consistency between training and live scoring |
| Reproducibility | Seeds, pinned environments, artifact registry | Repeatable experiments and audit-ready runs |
The result: reliable inputs reduce instability, speed development, and improve model performance, turning good data practices into measurable business value.
Operational Excellence: Resource Optimization and Cost Control
We apply right-sizing and autoscaling policies so infrastructure adapts to load with minimal human intervention, keeping costs aligned to real demand.
On-demand scaling and efficient infrastructure utilization
We monitor model resource usage and instrument pipelines to surface actionable insights that guide concurrency, batch sizes, and instance choices.
- Right-size strategies and autoscaling rules that match compute to workload while protecting SLAs.
- Use of spot capacity and reserved instances to lower runtime costs without sacrificing performance.
- Observability into resource consumption so teams tune workflows, cache artifacts, and reuse features to shorten runtimes.
- Placement across cloud and on-prem resources to balance latency, compliance, and cost objectives.
- Policies to suspend idle workloads and remove unused artifacts, cutting storage and compute waste.
- Cost attribution tools that assign spend to teams and projects, driving accountability and continuous optimization.
Operational gains free budget for innovation while preserving reliability and user experience, and our vendor-agnostic approach lets clients optimize across environments with existing tools.
| Strategy | What We Do | Business Benefit |
|---|---|---|
| Right-sizing & Autoscaling | Define instance profiles, autoscale thresholds, and concurrency limits | Lower idle cost, maintain performance |
| Cost-aware Placement | Place workloads by latency, compliance, and price across cloud/on-prem | Balanced cost and user experience |
| Observability & Attribution | Track resource metrics, per-project chargeback, alerts for anomalies | Transparent spend, continuous efficiency gains |
| Workflow Optimization | Cache intermediates, reuse features, suspend idle pipelines | Shorter runtimes, reduced storage and compute waste |
Vendor-Agnostic Architecture and Hybrid Flexibility
We design neutral architectures that let organizations run workloads where they make the most business sense — cloud, on-premise, or a mix. Our approach prioritizes business outcomes and total cost of ownership over allegiance to any single vendor.
Avoid lock-in while optimizing platform and tooling choices
We combine open-source tools with commercial frameworks to deliver stable, enterprise-ready solutions without long-term constraints. Hybrid patterns enable burst scaling, data residency, and the use of specialized accelerators where needed.
- Portability via containerization, standardized interfaces, and infrastructure as code reduces migration friction.
- Tooling strategies balance freedom and support, improving developer productivity and easing development workflows.
- We evaluate platforms against ecosystem maturity, compliance, and integration with existing infrastructure.
| Capability | What We Do | Benefit |
|---|---|---|
| Hybrid Runs | Cloud + on-prem orchestration | Flexibility for cost and compliance |
| Tool Mix | Open-source + commercial support | Operational stability with choice |
| Governance | Portable security & policies | Consistent controls across environments |
Our consulting process tailors stacks to team skills and roadmaps, de-risking long-term commitments and speeding time to deploy new capabilities.
Industry Use Cases: Finance, Healthcare, Retail, Manufacturing, and Telco
Our work turns raw telemetry and business events into repeatable learning models that sustain production performance. We tailor pipelines to each sector’s requirements, balancing latency, auditability, and continuous improvement so models keep delivering value.
Finance — Fraud and Risk
We build fraud detection pipelines that combine near-real-time scoring with strict governance and explainability, enabling rapid response while preserving compliance and audit trails.
Healthcare — Diagnostics and Predictive Care
Healthcare solutions use auditable data flows, explainable outputs, and HIPAA-aligned privacy controls so machine learning models support diagnostics and predictive care with enterprise risk standards.
Retail — Personalization and Inventory
Retail systems adapt recommendations and inventory signals in near real time, using continuous learning loops to improve conversion, reduce stockouts, and raise customer lifetime value.
Manufacturing — Predictive Maintenance and Quality
Sensor-driven models forecast equipment failure, optimize spare-part logistics, and catch quality regressions early, minimizing downtime and lowering operating cost while improving product quality.
Telecom — Churn and Network Optimization
Churn prediction and network models link insights to targeted retention actions and service routing, improving service quality and subscriber economics through scalable deployments and drift detection.
- Cross-industry patterns: feature stores, CI/CD, and monitoring keep models performant as data shifts.
- Domain tailoring: we map latency, reliability, and audit needs to each deployment.
- Measured outcomes: revenue lift, risk reduction, and cost savings demonstrate clear business success supported by our services and expertise.
Engagement Model: Consulting, Roadmapping, and Ongoing Support
We open conversations with stakeholders to map priorities, constraints, and the shortest path to demonstrable value. Our approach begins under an NDA so teams can speak freely about data, risks, and timelines.
Align objectives, define challenges, and deliver continuous proof of value
We start with consulting workshops that align sponsors on goals, constraints, and success criteria, creating shared ownership before any technical work begins.
- Problem definition: We define the challenge precisely, validate feasibility, and assess data quality so expectations and risks are clear.
- Roadmap & milestones: We co-create a roadmap that sequences development to production, embedding proof-of-value checkpoints and acceptance gates across the lifecycle.
- Processes and workflows: We design repeatable process flows and quality gates, documenting runbooks and handoffs to support continuous delivery.
- Team augmentation: Our team integrates with yours, transferring knowledge and codifying best practices for sustainable operations.
- Ongoing services: We provide monitoring, incident response, optimization sprints, and automated retraining to address drift, cost, or performance issues.
- Insights and reporting: Dashboards and regular reviews translate technical signals into business insights so leaders make informed decisions.
- Adaptive cadence: We revisit plans as new data and priorities emerge, keeping momentum and alignment throughout the engagement.
| Phase | Key Deliverable | Outcome |
|---|---|---|
| Discovery | Workshops, problem statement, NDA kickoff | Aligned goals, clear risks, confidential dialogue |
| Roadmapping | Milestones, proof-of-value checkpoints, timeline | Predictable delivery, measurable progress |
| Delivery & Support | CI/CD workflows, monitoring, optimization sprints | Stable production, reduced ops burden, continuous improvement |
We combine consulting experience with hands-on delivery, translating technical progress into business outcomes, and keeping communication transparent and pragmatic at every step.
Get Started: Free Consultation to Accelerate Your ML Operations
Schedule a secure, no-cost consultation so we can align on priorities, assess constraints, and propose a clear path to measurable success under NDA.
Schedule a call and discuss your goals under NDA
Book time with a senior technical expert to review requirements, architecture diagrams, and current workflows in detail. We protect confidentiality and focus on practical, prioritized outcomes.
- We invite you to a free, NDA-backed consultation where we discuss goals, constraints, and timelines with complete confidentiality.
- Our team prepares by reviewing your architecture, workflows, and services so recommendations fit your context and tools.
- Expect a capability assessment, maturity mapping, and a prioritized plan to accelerate operations with quick wins and longer-term initiatives.
- Access senior expertise, not generic forms, so you leave with actionable next steps and realistic effort estimates.
- We follow up with a concise summary, risk notes, and an initial roadmap that targets demonstrable success in the first 90 days.
- Contact options: US and India phone numbers, email for Puredot India, and a no-obligation WhatsApp chat with real experts (no bots).
| Next Step | Deliverable | Timeframe |
|---|---|---|
| Discovery call | Capability snapshot | 1 week |
| Technical review | Maturity map | 2 weeks |
| Roadmap | Prioritized plan | 4 weeks |
Conclusion
We accelerate the path from concept to reliable service, aligning engineering rigor with business outcomes, so teams realize measurable success faster and with less risk.
Our methodology can boost time to value by up to 5x and raise operational efficiency by up to 20% through thin-slice MVPs, CI/CD, AutoML, and cloud-native deployments that prioritize scalability and cost control.
We ensure sustained model performance with observability, drift detection, and automated retraining, while governance and security provide auditability for sensitive workloads and regulated industries.
If you want a clear, measurable path from development to deployment and improved user experience at the end of the pipeline, schedule a free, NDA-backed consultation and let us define the first steps toward production success.
FAQ
What services do you offer to streamline machine learning operations for U.S. enterprises?
We provide end-to-end services covering data engineering, model development, training, deployment, and production monitoring, combined with cloud-native infrastructure on AWS, Azure, and Google Cloud to ensure high availability, scalability, and reliability.
How does your thin-slice approach speed up time to value?
By focusing on thin-slice MVPs that validate core hypotheses quickly, we deliver working prototypes, iterate with CI-ready components, and accelerate learning cycles so teams realize value up to five times faster while reducing development overhead.
What practices do you use to ensure reproducibility and model quality?
We implement automated pipelines, experiment tracking, version control, and a model registry, alongside automated data validation, feature stores, and testing gates that enforce data and model quality throughout the lifecycle.
How do you handle continuous integration and deployment for machine learning?
Our CI/CD for machine learning includes automated testing gates for data and model validation, blue/green and canary rollout strategies with rollback, and production-ready repositories to maintain consistent deployments and fast iteration.
Can you integrate with our existing cloud and tooling choices?
Yes, we design vendor-agnostic, hybrid architectures that avoid lock-in while optimizing platform and tooling selection, enabling seamless integration with existing workflows, frameworks, and cloud providers.
How do you monitor model performance and detect issues in production?
We provide observability with distributed tracing, log analysis, anomaly detection, automated performance capture, and alerts for concept drift and data skew so teams can respond quickly and preserve business outcomes.
What governance and security measures do you implement across the ML lifecycle?
We enforce data and model governance with auditability, role-based access control, encryption in transit and at rest, and compliance workflows to meet regulatory and enterprise security requirements.
How do you help optimize infrastructure costs and resources?
We apply operational excellence practices including on-demand scaling, resource optimization, and cost control strategies to improve infrastructure utilization while maintaining performance under real-world loads.
What industry experience do you bring to finance, healthcare, retail, and manufacturing?
We have domain expertise delivering solutions for fraud detection, diagnostics, personalization, predictive maintenance, and churn prevention, combining technical rigor with product thinking to drive measurable business impact.
How do you measure success and demonstrate continuous value?
We align on business goals, define measurable KPIs, deliver roadmap-driven milestones, and run continuous proof-of-value engagements that show improvements in model performance, deployment frequency, and operational efficiency.
What is your engagement model for consulting and ongoing support?
We offer consulting, roadmapping, implementation, and managed services with collaborative teams, hands-on training, and long-term support to ensure capability transfer, process adoption, and sustained outcomes.
How quickly can we get started and what does the onboarding look like?
We begin with a free consultation under NDA to assess objectives, map value streams, and propose a targeted thin-slice plan; typical onboarding includes environment setup, initial pipelines, and a pilot MVP within weeks.