Quantiphi
AI-first digital engineering firm founded in 2013. Google Cloud Premier Partner and AWS Premier Tier Services Partner with consecutive Partner of the Year awards across both ecosystems. Differentiator is applied AI/ML depth — not generic migration — delivered through proprietary platforms (baioniq, Codeaira, QDox, Dociphi) and an India-anchored offshore delivery model. ~3,100 employees; ~2,700 based in India.
Summary
Quantiphi is a AI/ML Engineering Specialist firm (~3,100 employees (per LinkedIn data, Q2 2026); 3,500+ cloud-certified professionals (self-reported)) focused on Applied AI and Generative AI across GCP/AWS/Azure, with delivery experience in Healthcare & Life Sciences and Financial Services & Insurance.
Last reviewed: 2026-05-23 · Cloud Intel — independent, no paid placement
Quantiphi Analysis
✓ Strengths
- • Consecutive Google Cloud Partner of the Year — four categories in both 2025 and 2026; also won 2024 (three categories), 2021, 2020, 2019, 2017, 2018 ML awards — sustained recognition, not a one-off
- • AWS Premier Tier Services Partner (since July 2022) + AWS Public Sector Global GenAI Consulting Partner of the Year 2025; named First Preferred Amazon Quick Global SI Partner by AWS GenAI Innovation Center
- • Proprietary AI platform stack: baioniq (enterprise GenAI), Codeaira (software engineering agents), QDox (intelligent document processing on AWS), Dociphi (insurance/financial services IDP) — reduces build time and demonstrates IP depth
- • 3,500+ cloud-certified professionals; analyst recognition as Leader in ISG Provider Lens (AWS, Google Cloud, GenAI services), Gartner Emerging Leader in GenAI Consulting 2025, HFS Challenger Series March 2026
- • NVIDIA partnership and named as one of NVIDIA's top 13 Americas partners for fast-growing AI businesses; Snowflake Elite tier partner; Google Cloud delivery partner for Life Sciences (AlphaFold2, Vertex AI)
⚠ Considerations
- • Offshore-heavy delivery model (~87% of headcount in India) — onshore coverage is thin relative to total size; buyers needing significant US-based on-site presence should verify named team composition
- • Pricing is entirely opaque — custom private offers only; budget planning requires a scoping call before any estimates are possible
- • Azure presence is thinner than Google Cloud or AWS; Azure engagements are offered but not a stated focus, and no Azure-specific awards or specializations are publicly documented
- • Employee satisfaction signals are mixed: AmbitionBox shows 3.2/5 across 828 India-based reviews with concerns about work-life balance, job security, and hire-and-fire practices — relevant when evaluating delivery team stability
- • Gartner positions Quantiphi as an 'Emerging Leader' not a full Quadrant leader — appropriate calibration for a mid-sized specialist vs. global SIs
Best Fit For
- ✓ Enterprises needing production-grade AI/ML on Google Cloud — especially GenAI, Vertex AI, Contact Center AI, or life sciences workloads
- ✓ AWS-anchored organizations modernizing data platforms or deploying agentic AI (Bedrock, AgentCore) who want a specialist over a generalist SI
- ✓ Healthcare payers/providers and insurance firms requiring intelligent document processing with HITRUST/HIPAA compliance
- ✓ Mid-market and enterprise buyers willing to accept an offshore-heavy delivery model in exchange for AI depth and cost efficiency
Quantiphi Cloud Projects
DentalXChange — AI Document Processing for 30M Dental Claims (AWS)
DentalXChange, a revenue cycle management platform serving ~90,000 dental offices, replaced a template-dependent OCR system with Quantiphi's Qdox intelligent document processing solution on AWS. Built on Amazon Textract with custom ML models for ADA claims forms. Deployed serverless (Lambda) replacing ECS clusters, reducing infrastructure costs.
- → 98%+ accuracy in data extraction (vs. error-prone OCR baseline)
- → Manual document review rate reduced from 40% to 1%
- → Scaled to handle 30 million dental claims annually
- → Certain processing tasks reduced from hours to seconds
Cerevel Therapeutics — Clinical Workflow AI on Google Cloud
Built an end-to-end Doc AI solution on Google Cloud for Cerevel Therapeutics (neuroscience biopharma) to automate processing of clinical site monitoring trip reports. Automated review, extraction, and storage of information across four report templates with a custom UI for action-item visualization.
- → 99% accuracy in business rule execution
- → 93% accuracy in entity extraction
- → Eliminated manual backlog processing and reduced time-consuming review cycles
Arkansas Department of Health — Clinical Lab Report Processing (AWS)
Deployed QDox on AWS to process clinical laboratory reports received from multiple labs in varying formats. The system automatically classifies reports and extracts lab test name, result, and sending facility, operating under HIPAA compliance with encryption at rest and in transit.
- → ~20,000 monthly lab reports processed automatically
- → Reduced manual intervention in public health data workflows
- → HIPAA-compliant deployment with HITRUST-certified data handling
Quantiphi Pricing Indication
Pricing varies based on project complexity, duration, and specific requirements. Contact the partner for a detailed quote.
Questions to Ask Quantiphi
Before engaging with Quantiphi, here are key questions to help you evaluate fit:
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Team Composition: " What percentage of the team assigned to our engagement will be onshore (US-based) vs. offshore? Can we interview the lead architect and delivery manager before signing?"
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Platform vs. Custom Build: " Which of your proprietary platforms (baioniq, Codeaira, QDox, Dociphi) would apply to our use case? What does the licensing or access model look like, and do we retain ownership of custom models trained on our data?"
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Google Cloud vs. AWS Depth: " Our workloads are split between Google Cloud and AWS. Which platform does the proposed team have more recent delivery experience on, and how is the team structured across both?"
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Pricing and Scope: " What is your typical engagement structure for a project of this scope — fixed fee, time-and-materials, outcome-based, or pod model? What does a mid-range project cost, and what drives variation?"
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From Pilot to Production: " You mention moving customers from 'pilot purgatory' to production. What specifically does your TSaaS model change in the delivery approach, and what obligations does our internal team take on post-engagement?"
Red flags to watch for:
- ⚠ Offshore team composition disclosed only after contract signing — get the named team and offshore/onshore split in writing upfront
- ⚠ Vague platform licensing terms for baioniq or QDox — confirm data ownership, model portability, and what happens if the engagement ends
- ⚠ Generic GenAI advisory that doesn't map to a specific platform (GCP or AWS) — Quantiphi's value is platform-depth; a cloud-agnostic pitch from them is a yellow flag
- ⚠ No named case studies in your industry — their track record is real but concentrated in healthcare, insurance, and public sector; verify fit for other verticals
- ⚠ Outcome-based TSaaS framing without a clear definition of what 'outcomes' are measured and how disputes are resolved
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Related Research
Quantiphi — frequently asked questions
Is Quantiphi a good cloud consulting firm?
Quantiphi is a AI/ML Engineering Specialist firm specializing in Applied AI, Generative AI, Machine Learning across GCP and AWS and Azure, with delivery experience in Healthcare & Life Sciences and Financial Services & Insurance. Cloud Intel evaluates firms on partner tier, real case studies, and pricing transparency — not paid placement.
How much does Quantiphi cost?
Quantiphi operates in the Custom / private offer only — no published rate card. Offshore-heavy delivery model suggests cost advantage vs. pure-onshore peers; engagement size not publicly disclosed. pricing range. Final cost depends on project scope, duration, and complexity — contact them directly for a tailored quote.
What is Quantiphi best known for?
Quantiphi specializes in Applied AI, Generative AI, Machine Learning with core delivery across GCP and AWS and Azure. Additional competencies include Data Engineering, Cloud Modernization.
Which industries does Quantiphi serve?
Quantiphi primarily serves clients in Healthcare & Life Sciences, Financial Services & Insurance, Public Sector & Education, Media & Entertainment, Retail, Gaming. Buyers in these verticals are typically well-matched to their delivery experience and existing case-study base.
Who should consider Quantiphi?
Quantiphi is a strong fit for: Enterprises needing production-grade AI/ML on Google Cloud — especially GenAI, Vertex AI, Contact Center AI, or life sciences workloads; AWS-anchored organizations modernizing data platforms or deploying agentic AI (Bedrock, AgentCore) who want a specialist over a generalist SI; Healthcare payers/providers and insurance firms requiring intelligent document processing with HITRUST/HIPAA compliance.
Key Facts
- Headquarters
- Marlborough, MA (US HQ); major delivery centers in Bengaluru, Mumbai, and Thiruvananthapuram, India
- Founded
- 2013
- Team Size
- ~3,100 employees (per LinkedIn data, Q2 2026); 3,500+ cloud-certified professionals (self-reported)
- Industries
- Healthcare & Life Sciences, Financial Services & Insurance, Public Sector & Education, Media & Entertainment, Retail, Gaming
- Data Verified
- May 23, 2026
- Data Version
- Q2-2026
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