Data & feature foundations
Curated datasets, labeling workflows, PII tagging, and feature pipelines with access controls that mirror your enterprise directory.
Solution
AI Solutions
Production ML, LLM copilots, retrieval and evaluation harnesses, MLOps, FinOps for inference, and governance patterns that satisfy model risk and privacy reviewers, with documented evaluation gates and rollback for every production model.
AI Solutions cover the full path from first model in production to fleet-scale copilots: data pipelines, feature stores, training and fine-tuning, evaluation harnesses, deployment behind your API gateway, and monitoring for drift, safety, and cost. We align with your existing risk tiering for what can be fully automated, what needs human-in-the-loop, and what stays off limits for generative interfaces.
For LLM-powered experiences, we implement retrieval with permission-aware document indexes, citation patterns executives trust, prompt and tool policies, and red-team cycles before broad rollout. For classical ML, we emphasize reproducible training, model cards, and lineage so when a score or recommendation is challenged, you can explain inputs and versions.
Neojn integrates with your MLOps stack or helps stand one up: experiment tracking, registry, canary and shadow deployments, and FinOps views so finance sees token spend and GPU burn alongside business KPIs. Security covers data residency, PII handling, and supply-chain vetting for open-weights and third-party APIs.
We maintain a register of model and vendor dependencies with version pinning, exit plans, and substitution tests so a provider outage or license change does not strand production workflows without a rehearsed fallback.
Governed intelligence, not shadow IT
Every engagement defines success metrics, evaluation sets, and rollback triggers before launch. Stakeholders from legal, security, and product share the same dashboards for latency, quality, and policy violations.
Capabilities across the AI lifecycle
From batch scoring to interactive assistants, built to pass internal and external scrutiny.
Model development & fine-tuning
Training jobs, hyperparameter search, and domain adaptation, with experiment lineage and reproducible environments.
Evaluation & safety
Offline and online metrics, bias and robustness checks, jailbreak and prompt-injection test suites tailored to your use cases.
RAG & knowledge assistants
Chunking, embedding, and retrieval tuned to your corpus; guardrails for tool use and outbound actions.
Production serving & scale
Autoscaling inference, caching, batch vs real-time tradeoffs, and SLOs wired to paging and executive reporting.
MLOps & FinOps
Model registry, deployment gates, cost attribution per workload, and chargeback views for platform teams.
What changes after go-live
Organizations work with us to ship AI that survives real traffic, regulatory questions, and board reviews, not demos that stall in pilot purgatory.
- Measured lift on the KPIs you named in the charter, with holdouts and guardrails documented.
- Faster iteration cycles because data scientists and engineers share tooling and definitions.
- Reduced incident anxiety with playbooks for model rollback, content policy breaches, and vendor API outages.
Enterprise AI solutions, LLM governance, and MLOps at scale
Search demand clusters around enterprise AI solutions, LLM governance for regulated companies, MLOps consulting, and responsible AI deployment, topics that only matter when models reach production with evaluation harnesses, rollback triggers, and FinOps visibility. Neojn ties each engagement to those operational realities rather than prototype demos.
Retrieval-augmented generation, copilots, and classical scoring models share requirements: lineage, access controls aligned to your directory, and incident playbooks for drift, unsafe outputs, and vendor outages. We document those alongside business KPIs so model risk and internal audit reviewers see the same artifacts product teams use.
When budgeting inference, token spend and GPU utilization should appear next to revenue or cost outcomes. Our FinOps views help CFO and platform forums fund growth without surprise spikes after launch.
AI solutions: FAQs
What leaders ask before funding the next AI phase beyond a pilot.
Data classes drive tagging, masking, and residency choices. Retrieval indexes respect document ACLs; prompts and outputs are logged according to your retention policy.
AI delivery phases that survive scrutiny
From assessment to fleet operations with clear owners at each gate.
Use-case charter and data readiness
We validate data access, evaluation sets, and policy boundaries before engineering sprints consume budget.
Build, evaluate, red-team
Models and prompts ship with automated tests, human review steps, and documented rollback for regressions.
Canary and shadow traffic
Production entry matches your risk tiering: shadow scoring, partial traffic, or full rollout with SLO-based paging.
Operate and improve
Drift monitors, cost attribution, and periodic red-team cycles keep programs compliant as data and vendors evolve.
Programs that pair with AI work
Platform, data, and security services most AI roadmaps already depend on.
Data & AI services
Lakehouse patterns, catalogs, and quality pipelines that feed trustworthy features and documents.
Data & AICybersecurity
Zero trust, logging, and third-party risk evidence for models and APIs exposed internally or externally.
CybersecurityHealthcare & life sciences
Clinical and research constraints when AI touches PHI or regulated evidence.
Healthcare & life sciencesInsights articles
Ongoing ideas on governance, evaluation, and FinOps for AI platforms.
Browse articles
Pressure-test your AI roadmap
Bring one production candidate or idea; we will stress-test data readiness, evaluation plans, and governance gaps in a working session, with a short list of decisions and owners before you fund the next phase.