Move from AI pilots to production systems

Artificial Intelligence

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The problem

Everyone is talking about AI. Almost no one is shipping it.

Promising prototypes aren't enough. When you're fighting siloed data, non-existent MLOps, and complex compliance regulations, bridging the gap from 'concept' to 'measurable business value' is nearly impossible alone.

You don't need another AI proof-of-concept. You need an engineering partner to bridge the gap from experiment to enterprise-grade production.

Solution overview

AI that runs in production, not just in a notebook.

Tenjumps builds AI systems designed for production from the start. We connect AI to your data foundation, engineer the full model lifecycle, deploy with governance and compliance built in, and automate operations so your AI investment scales. Every engagement is grounded in real business use cases, not technology demos.

01

GenAI & Intelligent Agents

From Prototype to Autonomous Workflows

Agentic Orchestration: Autonomous agents that execute complex workflows, automate business processes, and generate code.
Enterprise RAG: RAG-powered knowledge bases and virtual assistants trained securely on your proprietary data.
Smart Applications: AI-integrated CRM/ERP extensions and industry-specific intelligent web and mobile apps.
Trust Layer: Built-in compliance guardrails, policy enforcement, and rigorous bias/fairness evaluation.

02

ML & Model Operations (MLOps)

Hardening the Model Lifecycle

Engineering Excellence: End-to-end management from feature engineering and hyperparameter tuning to experiment tracking.
High-Scale Deployment: Real-time REST endpoints, batch scoring pipelines, and streaming inference.
Rigorous Governance: Versioning, automated approvals, and seamless roll-forward/rollback capabilities.
Continuous Observability: Active monitoring for drift, latency, and accuracy with automated retraining loops.

03

AI-Powered SDLC & DevOps

Accelerating Delivery with AI-First Engineering

Agentic Development: AI-assisted requirements discovery, automated code generation, and agentic "coders" to speed up delivery.
Quality & Testing: Automated test case creation and smart data labeling to ensure production readiness.
Automated Governance: AI-based architecture checks and design risk scoring for scalability and security.
Regulatory Readiness: Development workflows pre-configured for SOC2, PCI, and GDPR compliance.

04

Intelligent automation

Self-Optimizing Systems for Enterprise Scale

Process Evolution: Moving beyond RPA to intelligent workflow orchestration and API-led, self-healing automation.
AIOps & Infrastructure: Infrastructure as Code (IaC) paired with AI event correlation and auto-remediation.
Modern Operations: Automated environment provisioning, CI/CD optimization, and runbook automation.
Measurable Impact: Targeted outcomes of 20–40% productivity gains and a significant reduction in human error.

What our clients will see

Days to production systems

Days to production

8-30 days

Productivity gains

20-40%

Efficiency gains

60%+

Improvement in data accuracy

90%+

90%

Why companies choose Tenjumps

Tenjumps builds production-grade AI systems that actually scale. By integrating robust data foundations with end-to-end MLOps and built-in compliance, we transform AI from a conversation into a competitive advantage. Every engagement is grounded in real business use cases, not technology demos.

We build the data foundation and the AI on top of it

Most AI consultancies assume your data is ready. It usually is not. Tenjumps spans data strategy, data engineering, and AI under one team, which means we can fix your data foundation and deploy production AI in a single engagement instead of two. No handoffs between a data vendor and an AI vendor. No waiting six months for the data platform before AI work can start.

Senior engineers from day one, not after the sale

Our pods are led by engineers with 30+ years of individual experience who have deployed AI and ML systems across logistics, financial services, and manufacturing. The team that designs your AI strategy is the same team that builds and deploys your models. No junior bench-padding, no handoffs.

Built to hand off, not to lock in

Every AI engagement includes knowledge transfer, model documentation, governance runbooks, and team training. We build reusable model and prompt libraries so your internal team can extend and maintain AI systems independently. If you need ongoing support, our operations pods are available. But the goal is always your self-sufficiency.

Success stories

Results that speak for themselves

60%

Instant resolution for global logistics

The Challenge: A logistics leader was overwhelmed by 150+ daily emails—83% of which were repetitive shipping queries.

The Solution: Tenjumps deployed an AI chatbot trained on historic email patterns in just 60 days.

The Result: 60% of tickets resolved automatically without human intervention.

24/7 global support across 200+ countries.
CS reps redirected to high-value, complex cases.

99%

Faster candidate verification for fintech

The Challenge: A financial services firm had a 4-month hiring lag due to manual recruiter verification.

The Solution: We built an agentic AI solution in only 10 days to automate re-engagement and LinkedIn verification.

The Result: 70% candidate re-engagement with 90% matching accuracy.

Delivery time slashed from 4 months to 4 weeks.
Eliminated weeks of manual searching for the team.

Featured

Read our latest insights on enterprise AI

How we evaluate, deploy, and govern AI with your team.

From AI use case to production in four stages

Our Business Excellence Model (BEM) is designed to move AI from experiment to enterprise-grade production. One team owns the entire journey: data readiness, model development, deployment, governance, and ongoing optimization.

01 Explore

Strategy & Readiness

We audit your data foundation and infrastructure to identify high-value use cases. The output is a prioritized roadmap based on technical feasibility and business ROI.

02 Engage

Architecture & Governance

We select the right tech stack—RAG, agents, or ML—and design for scale. For regulated industries, we bake in compliance frameworks and guardrails before a single line of code is written.

03 Execute

Agile Deployment

Our engineering pods build and ship. Whether it’s GenAI agents, MLOps pipelines, or intelligent automation, we deploy with full observability, auditability, and governance from day one.

04 Evolve

Optimization & Autonomy

We monitor for drift, bias, and performance, building feedback loops for continuous retraining. Our goal is to mature your internal AI capability so you own the platform.

Insights from our team

Explore all insights

Cost of poor data quality

A single data quality issue cost 50 engineering hours last quarter. Only 6 were tracked. Paleti Lakshmikanth breaks down where the hidden time goes.

Production data engineering: what tutorials never teach you

Production data engineering looks nothing like tutorials. Kavya Kumari shares what actually changes when pipelines run at scale and stakeholders are waiting.

Cloud cost optimization: The 50GB report nobody reads

For the 50GB weekly export, 47 recipients receive it, but only 3 open it. Bhavya Venu breaks down how wasteful data exports drain cloud budgets and what to do about it.

FAQs about AI consulting

Our data is not ready for AI. Can you still help?

Yes, and this is one of the most common starting points for our engagements. Tenjumps spans data strategy, data engineering, and AI under one team. We can assess your data foundation, fix governance and quality gaps, and deploy production AI as part of a single engagement. You do not need to hire one vendor for data and another for AI.

What is the difference between AI agents and traditional automation?

Traditional automation (RPA, rule-based workflows) follows predefined steps. AI agents can reason, make decisions, and adapt to new inputs. For example, an RPA bot fills out a form the same way every time. An AI agent can read a customer email, understand the intent, look up the relevant data, draft a response, and escalate complex cases to a human. We build both, and we help you determine which approach fits each use case.

How do you handle AI compliance and governance?

Every AI deployment includes guardrails, auditability, policy enforcement, and bias detection and fairness evaluation. For ML models, we implement version registration, approval workflows, rollback capabilities, and continuous monitoring for prediction accuracy, drift, latency, and bias. For regulated industries, we design compliance frameworks aligned to SOC2, PCI, and GDPR requirements, with governance dashboards for visibility into model behavior and data usage.

How long does it take to deploy an AI system?

It depends on the use case and your data readiness. Simple AI agents and chatbots can be in production in 10-30 days. ML models with full governance and monitoring typically take 4-8 weeks. Large-scale intelligent automation programs are scoped during the Explore phase. Our BEM delivery model is designed to show measurable progress in the first month regardless of total scope.

What AI technologies and platforms do you use?

We build on Databricks for ML workflows (MLflow, feature store, model serving, Mosaic AI), LangChain for agent and RAG architectures, and vector databases for semantic search. For orchestration, we use Databricks Workflows, Airflow, and Temporal. Our cloud expertise spans AWS, Azure, and GCP. We select the right technology for each use case rather than defaulting to a single stack.

What industries do you deploy AI in?

Our AI practice serves logistics, financial services (BFSI), and manufacturing, with applications spanning legal, risk, and compliance; business operations; customer and citizen services; IT and application operations; and industry-specific use cases. We bring domain knowledge to every engagement, which means our AI systems are designed for your business context, not generic templates.

How does Tenjumps pricing compare to larger AI consultancies?

Our teams are senior-only, which means a higher day rate than offshore-heavy firms but significantly fewer total days to production. Our AI-assisted delivery model further compresses timelines by automating routine engineering tasks. Clients typically see production AI systems in weeks rather than the months common with larger firms, and mid-market clients average an 8-month payback period.

Ready to move AI from pilot to production?

Tell us where you are with AI today and we will show you the fastest path to production systems that deliver measurable business value.