Predictive models & analytics
Forecasting, classification, scoring, and anomaly detection wired to operational data with drift monitoring and retrain triggers.
AI/ML Engineering
Machine learning systems built for production, not demos.
Machine learning, predictive models, NLP, and computer vision, engineered in your environment with clear evaluation gates, integration paths, and full IP transfer at handover.
Measured
Models tied to business KPIs
Governed
Training data & lineage controls
Integrated
APIs into apps & workflows
Owned
Artifacts & code at handover
Perspective
Engineering intelligence that survives production
Most ML initiatives stall between notebook experiments and systems operators can trust. The gap is rarely the algorithm. It is data contracts, feature pipelines, evaluation discipline, and deployment patterns your platform team can run.
InheritX delivers AI/ML engineering as production capability: predictive models, NLP pipelines, and computer-vision systems scoped to workflows that move revenue, cost, or risk, not science projects without an owner.
Every engagement ends with models, training pipelines, and integration code your teams own, deployed inside your security boundary with monitoring hooks your SRE or ML platform group can sustain.
Capabilities
Core engineering lanes
Named to match how architecture and product teams scope work, delivered as integrated systems, not disconnected model files.
NLP & language understanding
Entity extraction, classification, summarization, and domain-specific text pipelines with evaluation suites drawn from real cases.
Computer vision
Detection, segmentation, and visual inspection models for operations, quality, and safety, including edge deployment when data cannot leave site.
Feature & training pipelines
Reproducible data prep, versioning, and training workflows that platform teams can promote through staging to production.
Dual view
What production ML requires
Patterns we avoid
Notebook-only deliverables with no serving path
Models trained on snapshots with no refresh plan
Black-box APIs with no lineage or rollback
What we deliver
- Serving endpoints or batch jobs aligned to SLAs
- Golden datasets and regression gates before promotion
- Observability for latency, accuracy, and data drift
- Documentation and runbooks for internal operators
Fit
When AI/ML engineering is the right move
Use this lens with your architecture board, not as a sales checklist.
Decisions depend on structured and unstructured signals
Hybrid models with feature stores and governed training data
Accuracy must be explainable to business owners
Evaluation reports, error analysis, and human review on edge cases
Models must run beside existing applications
API-first integration with your identity and audit patterns
Vision or language tasks are domain-specific
Custom training on your imagery or corpora with transfer learning where it helps
Continue
Adjacent capabilities
FAQ
AI/ML Engineering FAQ
No. We use transfer learning, foundation models, and classical ML where each fits. The goal is the right accuracy-cost-latency tradeoff for your workflow.
Yes. We align to your lakehouse, warehouse, or feature store patterns rather than introducing parallel infrastructure without cause.
We hand over serving code, monitoring dashboards, and retrain playbooks. Embedded support is available during stabilization.
AI/ML Engineering covers predictive, NLP, and vision systems broadly. Generative AI is the LLM, RAG, and copilot lane when language generation is the core product pattern.
Next step
Map this capability to your mandate.
A focused strategy conversation, constraints, systems, and what production readiness looks like for your organization.