Enterprise AI Reliability Engineering

Your AI is live. Prove it actually works.

We evaluate, build, and monitor AI systems so they work in production — not just in demos. Structured testing and measurable evidence for every engagement.

0%

Enterprise AI projects fail to deliver ROI

$0B

Cost of AI hallucinations globally in 2025

0%

AI proofs-of-concept never reach production

$0B

Addressable AI services market by 2027

Why AI Projects Fail in Production

The gap between a demo and a dependable system.

Hallucination Without Guardrails

Models confidently produce false information with no detection mechanism in place.

$112B global cost in 2025

Broken RAG Pipelines

Poor chunking and retrieval strategies return irrelevant context, degrading answer quality.

88% of POCs never reach production

Agentic Systems With No Observability

Multi-step agents fail silently — no traces, no state management, no recovery.

80% fail to deliver ROI

Demo-to-Production Gap

Systems that impress in demos break under real-world data, scale, and edge cases.

$300B addressable market by 2027

How We Solve It

A five-step reliability pipeline.

01

Discover

Audit existing AI systems, prompts, and workflows.

02

Evaluate

Score performance with LLM-as-judge benchmarks.

03

Optimise

Redesign prompts, retrieval, and agent logic.

04

Validate

Re-test with measurable before/after evidence.

05

Monitor

Ongoing tracking with monthly trend reports.

Services

Engagements scoped for enterprise reliability.

Evaluate

AI Quality Audit

Score any AI system 0–100 on accuracy, relevance, safety, and compliance — with a before/after comparison.

LLM-as-judgeTest design
Build

RAG Pipeline Design & Build

End-to-end retrieval-augmented generation systems — ingestion, chunking, embeddings, vector DB, hallucination controls.

LangGraphLanceDB / PineconeClaude / GeminiAWS
Build

Agentic Workflow Architecture

Multi-step autonomous agents with observability, state management, and retry logic — integrated with your systems.

LangGraphn8nMCP ProtocolAWS Lambda
Monitor

Continuous Monitoring Retainer

Monthly evaluation runs, trend reports, anomaly alerts, and ongoing optimisation for live AI systems.

Monthly eval runsMoM trendsSlack access
Flagship

Enterprise AI Reliability Sprint

A 4–8 week comprehensive engagement: full audit, RAG build or remediation, agent architecture, compliance validation, documentation, and a 90-day monitoring retainer.

Why Tvaksa for AI

Established credibility. Applied AI science.

CapabilityGeneric VendorTvaksa
Enterprise eval experienceLimitedYes
Measurable evidenceAnecdotalBefore/after scores
Existing trust foundationNoneCapgemini, Pearson VUE, AWS

Enterprise evaluation experience across live AI products

Applied science methodology — LLM-as-judge, structured test cases

Measurable, evidence-based optimisation reports

For Business Partners

Partnership models for B2B contacts.

Referral

Refer enterprise clients to Tvaksa for AI evaluation and build engagements — earn referral commission.

Subcontractor

Bring Tvaksa in as the AI engineering subcontractor on your existing client engagements.

White-Label

Deliver Tvaksa's AI evaluation and build services under your own brand to your clients.

FAQ

Common questions.

Ready to build the AI reliability layer your enterprise needs?

Get In Touch

Talk to the AI Services team.

AI Services Contact

AI Quality Audits, RAG pipelines, agentic workflows, and continuous monitoring.

Email

ai@tvaksatech.com

Phone

+91 70260 02096

Hours

Calls: 9:00 AM – 6:00 PM | WhatsApp & Message: Anytime

Book a call

Send us a message

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