AI Consulting Services That Help Build & Scale AI

AI consulting defines the strategy and roadmap. Machine learning development builds and deploys it. Consulting covers use case identification, data readiness assessment, vendor evaluation, and AI adoption planning. ML development covers model architecture, feature engineering, training pipelines, testing, integration, and MLOps.

For most enterprises, both are needed in sequence. Consulting without development stays theoretical. Development without consulting often builds the wrong thing. The overlap matters most at the start: scoping the right problem, assessing data quality, and setting measurable success criteria before any model work begins. Organizations that treat strategy and engineering as one connected engagement consistently move from prototype to production faster than those that separate them.

Kanerika’s AI and ML service covers both ends — strategic AI roadmap development and end-to-end ML engineering — through a single structured engagement model. The IMPACT methodology connects business assessment directly to technical delivery without a handoff gap.

The single most important question to ask is whether the firm builds production systems or only delivers strategy documents. Firms that have never run an ML model in a live enterprise environment cannot reliably advise on the challenges you will face when trying to do the same.

Beyond that, the evaluation checklist should cover:

• Industry depth: have they deployed ML in your vertical with the compliance and data constraints you face?

• Technology partnerships: are they certified on the platforms you already run — Azure, Databricks, AWS?

• Full lifecycle ownership: do they handle data pipelines, model training, integration, MLOps, and post-launch monitoring?

• Delivery track record: can they show case studies with named clients, specific metrics, and before/after baselines?

• Data security: do they hold ISO 27001, SOC 2, or equivalent certifications? Do they have a data privacy framework?

• Team transparency: who specifically will work on your project — not the pitch team, but the delivery team?

Kanerika is a Microsoft Solutions Partner for Data and AI, Databricks partner, holds ISO 27001, SOC 2, and CMMI Level 3, and has production ML deployments across Kroger, Siemens Healthineers, Sony, Volkswagen, and Zydus Cadila across eight industries.

ROI from ML projects is measurable only if a before-state baseline exists. The first step is agreeing on specific metrics before development begins: processing time per transaction, error rate on a specific workflow, cost per decision, or revenue per customer segment. Without a defined baseline, any claimed improvement is unverifiable.

The strongest ROI signals come from narrow, high-volume use cases where AI replaces or accelerates a clearly defined human task. Broader platform initiatives take longer to show returns. CFOs should expect:

• Time-to-value: focused use cases show measurable impact within 6-12 months post-deployment

• Direct cost savings: labor reduction on automatable tasks, error-related rework, compliance penalties avoided

• Revenue impact: faster decisions, better forecasting accuracy, improved customer experience metrics

• Hidden costs to track: model inference compute, ongoing monitoring, periodic retraining, data pipeline maintenance

• Deloitte research (2023) found a median ROI of 17% for companies with established ML programs

Kanerika sets ROI baselines at the start of every engagement. Published production outcomes include a 24% margin increase for luxury retail, $1.2M annual savings in logistics operations, and 50% faster time-to-market for fintech clients.

Most buyers focus on pricing and credentials. The questions that actually reveal partner quality are operational and specific.

• Can you show a live production deployment — not a demo, not a slide, but a running system?

• Who exactly will work on my project, and what is their background in my industry?

• How do you handle model drift and retraining after launch — is that included or billed separately?

• What does your data security architecture look like, and which certifications do you hold?

• How do you define success, and at what point in the project do we set those metrics?

• What happens if the model underperforms? Do you have a remediation process, and how is it billed?

• How do you transfer knowledge to our internal team so we are not permanently dependent on you?

MLOps applies software engineering discipline to the machine learning lifecycle. It covers everything that happens after a data scientist trains a model: packaging it for deployment, connecting it to production data

pipelines, serving predictions at scale, monitoring accuracy over time, detecting when the model drifts, and managing retraining and versioning.

Without MLOps, organizations end up with models that work in a notebook but fail in production, cannot be updated without a manual scramble, and have no audit trail for compliance teams. Most enterprise AI projects that get stuck in ‘pilot purgatory’ are not held back by bad models. They are held back by the absence of the operational infrastructure to run, maintain, and iterate on those models in production.

Kanerika’s AI Architecture and MLOps service builds scalable deployment pipelines, automated model monitoring, drift detection, and AI governance frameworks as standard components of every ML engagement — not add-ons.

Pricing varies widely by scope and provider type. Junior consultants typically range from $100 to $150 per hour. Senior ML architects and specialists charge $300 to $500 or more. Enterprise-level AI strategy engagements run $100,000 to $250,000. Complex custom ML implementations — covering data engineering, model development, integration, and MLOps — range from $200,000 to $500,000 or higher, with timelines of 8 to 18 months.

Three things buyers consistently underestimate in budget planning:

• Ongoing infrastructure costs: model inference compute, vector database hosting, monitoring tools — often comparable to the build cost over 12-18 months

• Data preparation: organizations with clean, structured data need significantly less pre-work than those with fragmented or untagged datasets

• Retraining and maintenance: models drift as real-world data changes, and refreshing them requires time and budget that should be planned upfront

Data readiness is the most underestimated prerequisite for ML. The questions that matter before any model development begins are: does the right historical data exist, is it clean and consistently formatted, is there enough of it for reliable pattern recognition, and can it be accessed without regulatory or technical blockers?

Most enterprises discover gaps during the assessment. Common findings: incomplete historical records that limit training window, inconsistent labeling across systems, PII restrictions that limit what data can enter a pipeline, and siloed datasets in different systems that need integration before they are useful together. A data readiness assessment at the start of an engagement prevents teams from building models on unfit data — one of the most frequent reasons ML pilots fail to reach production.

Kanerika conducts a structured data readiness and use-case assessment as the first phase of every AI and ML engagement. The free AI Maturity Assessment at kanerika.com lets organizations self-evaluate before the first conversation.

A focused, well-scoped ML deployment for a single use case — demand forecasting, anomaly detection, document classification, pricing optimization — typically takes 8 to 12 weeks from kickoff to production with clean data and defined requirements. Proof-of-concept phases usually run 4 to 6 weeks.

More complex programs involving multiple models, legacy system integration, or regulated data environments take 3 to 6 months. Enterprise-wide AI transformation programs run 8 to 18 months. The most reliable predictor of timeline is data quality. Organizations with clean, accessible, well-labeled data move significantly faster than those requiring extensive data preparation before model work can begin. The second predictor is stakeholder alignment: projects with executive sponsorship and a clear decision-maker move from approval to production in weeks, not quarters.

Kanerika’s CMMI Level 3 process accreditation keeps delivery timelines predictable. The phased IMPACT methodology is designed to deliver early visible value — a working pilot — before full-scale deployment is approved, reducing investment risk.

Kanerika’s AI and ML practice covers three service areas. ML and NLP Advanced Services: natural language processing for document automation, ML-powered conversational interfaces, and text analytics for knowledge discovery. AI Architecture and MLOps: scalable deployment pipelines, AI governance frameworks, responsible AI policy implementation, and automated model monitoring. AI Solutions and Engineering: custom ML model design for complex business problems, end-to-end solution development with system integration, and strategic AI roadmap development.

Beyond consulting, Kanerika ships purpose-built ML models for specific industry use cases that run in production client environments: Sales Trends Forecasting (direct and indirect channel demand prediction), Smart Product Pricing (dynamic pricing based on market analysis), Inventory Optimization (ideal stocking level recommendations), Vendor Selection Advisory (data-driven logistics vendor ranking), Logistics Route Optimizer (multi-constraint route and capacity optimization), and Claims Adjudicator Copilot (similarity-based historical case matching for insurance).

Every engagement follows Kanerika’s IMPACT methodology with defined phases and measurable exit criteria at each stage. It opens with a business and data readiness assessment: identifying use cases that match the organization’s data maturity, scoping the first build phase, and agreeing on success metrics before any

development begins. The AI Maturity Assessment at kanerika.com allows organizations to self-evaluate across AI/ML foundations, generative AI capabilities, and agent deployment readiness before the first call.

From assessment, the team moves through model architecture design, data pipeline development, model training and validation, enterprise system integration, testing, and production deployment. Post-launch monitoring, model drift detection, and performance optimization are included as standard. The phased approach ensures early value is visible before full-scale rollout commits additional budget.

Kanerika has delivered AI and ML implementations across banking, insurance, logistics and supply chain, manufacturing, automotive, pharma, healthcare, and retail and FMCG. Named clients include Kroger, Siemens Healthineers, Sony, Volkswagen, Zydus Cadila, The Wonderful Company, HaulHub, KBR, Fortegra, and Trax.

Documented outcomes from production deployments:

• 24% increase in profit margins through AI-powered dynamic pricing for a luxury retail client

• 39% faster price change cycle time with 100% auditability of all pricing decisions

• 95% accuracy in counterfeit detection using AI vision, with 68% faster product verification

• 48% faster client preparation and 33% higher transaction value through AI-powered clienteling

• $1.2M average annual cost savings across logistics operations client deployments

• 50% faster time-to-market for fintech and healthtech product teams

• 30% reduction in project timelines for pharma clients using the IMPACT methodology

Data security and governance controls are built into Kanerika’s ML architecture from the start of every engagement. Standard controls include role-based access that scopes what each model and user can access, encrypted data pipelines in transit and at rest, immutable audit logs covering model decisions and data access, and bias testing as part of the validation process.

Kanerika holds ISO 27001, ISO 27701, SOC 2, and ISO 9001 certifications, plus CMMI Level 3 process accreditation. For regulated industries — banking, healthcare, pharma, insurance — data handling is designed for GDPR, HIPAA, and sector-specific compliance from the architecture stage. The AI Architecture and MLOps service includes responsible AI policy implementation and AI governance framework design as standard deliverables, not optional add-ons.

A focused single-use-case ML deployment with defined requirements and clean data typically runs 8 to 12 weeks from kickoff to production. Multi-use-case programs with multiple enterprise integrations or regulated data environments take 3 to 6 months. Enterprise-wide programs with multiple workstreams are scoped over 6 to 12 months.

Kanerika offers three engagement structures: fixed-scope project delivery for well-defined requirements, time-and-materials for exploratory or iterative builds, and managed deployment for ongoing model monitoring, retraining, and optimization post-launch. Pricing is not published publicly because scope varies significantly by use case, data readiness, and integration complexity. The AI Maturity Assessment provides a structured starting point to narrow scope before pricing discussions begin.

ROI from Kanerika’s AI and ML engagements is tracked against baselines agreed at the start of every project. The strongest returns come from high-volume, document-heavy, or decision-intensive processes where the before-state is well-measured.

Kanerika’s published page metrics cite a 95%+ client satisfaction rate, significant business task automation, and a 2x faster time-to-market as headline outcomes. From specific production deployments: luxury retail clients achieved a 24% margin increase through AI dynamic pricing, logistics clients average $1.2M in annual savings, pharma clients reduced project timelines by 30%, and fintech clients cut time-to-market by 50%. The team’s page also references automated 60% of business tasks as a portfolio metric. These are production figures, not projections.

Three things differentiate Kanerika in a market crowded with firms claiming AI expertise.

First, Kanerika builds and ships production ML models and AI agents. The AI Suite — Sales Trends Forecasting, Smart Pricing, Inventory Optimization, Vendor Advisory, Route Optimizer, Claims Copilot, Karl (data insights), DokGPT (document intelligence), Susan (PII redaction) — are live in client environments. The same engineers who build those products handle client delivery. That is a fundamentally different capability than a firm that only advises.

Second, strategic technology partnerships give the team certified depth on enterprise platforms: Microsoft Solutions Partner for Data and AI, Featured Microsoft Fabric Partner, Databricks partner with certified engineers. Third, CMMI Level 3 process accreditation and ISO 27001, SOC 2, ISO 27701 certifications matter specifically for enterprise and regulated-industry engagements where delivery reliability and compliance posture are evaluated alongside technical capability.

• Production ML models in live client environments — same team builds and delivers client projects

• Microsoft Solutions Partner for Data and AI + Featured Fabric Partner + Databricks partner

• CMMI Level 3, ISO 27001, SOC 2, ISO 27701 — for compliance-sensitive regulated environments

• Cross-industry production record: banking, logistics, pharma, automotive, retail, healthcare

• Named enterprise clients: Kroger, Siemens Healthineers, Sony, Volkswagen, Zydus Cadila, HaulHub