Provectus
AI-first systems integrator founded in 2010, headquartered in Palo Alto.
What is Provectus?
Provectus is an AI and cloud engineering consultancy founded in 2010 by Stepan Pushkarev, headquartered in Palo Alto with 500–1,000 employees across roughly nine locations. The company positions itself as a mid-market AI-first systems integrator, combining big-data engineering, cloud engineering, and applied ML/AI practices, and holds partner status with major cloud providers (per company website; independently unverifiable exact partnership tier).
Provectus was founded in 2010 and is headquartered in Palo Alto, California, USA. The firm employs 501–1,000 people and works primarily with clients in Retail, Healthcare, Financial Services, Technology/SaaS sectors. Its primary differentiator is: Combines AI/ML delivery with cloud and big-data engineering as a single integrated systems-integrator practice.
Provectus tech stack and services
| Service area |
|---|
| Custom ML Development |
| MLOps |
| Data Engineering |
| AI Consulting |
Provectus use cases
Short answer: Provectus is best suited for mid-market and enterprise buyers, AI bundled with cloud engineering.
| Use case |
|---|
| Consolidating a fragmented cloud + data + ML stack under one delivery partner |
| Standing up a big-data platform that feeds downstream ML models |
| Enterprise AI/ML programs that need a partner with cloud infrastructure depth, not just data science |
Provectus pricing
Short answer: Provectus uses a fixed project and dedicated team engagements pricing approach. Minimum engagement starts at $50K.
| Engagement model | Typical range | Best for |
|---|---|---|
| Fixed project | From $50K | Well-defined scope |
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
Provectus pros and cons
| Advantages | Things to consider |
|---|---|
| +15 years of continuous operation gives a longer delivery track record than most boutiques on this list | -Broader systems-integrator scope means ML-specialist depth is spread across cloud and data-engineering practices rather than singularly focused |
| +Combines data engineering and MLOps with model development, reducing hand-off friction between teams | -Mid-market pricing and minimums put it out of reach for very small pilot projects |
| +500–1,000 employee scale supports multiple concurrent enterprise workstreams | -Public reporting on exact current headcount varies by source (500–1,000 vs. ~700), so buyers should confirm team size directly |
| +Established cloud-provider relationships support production deployment at scale |
Provectus vs alternatives
How Provectus compares to the other top Machine Learning Development agencies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Neurons Lab | Regulated finance firms, PoC-to-production ML delivery. | One of the few AI consultancies worldwide holding AWS's Advanced Machine Learning Consulting Competence. | 4.8 | Full comparison |
| Tensorway | Mid-market companies, full-stack ML plus agentic AI. | full-stack ml delivery — data science, mlops, and llm/agentic frameworks (langchain, langgraph, autogen) — in one team. | 4.6 | Full comparison |
| InData Labs | Fintech, healthcare, SaaS — specialist data-science boutique. | Dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing. | 4.5 | Full comparison |
| AI Superior | EU SMBs, research-grade ML at accessible pricing. | PhD-founder-led team with an explicit research-and-development service line alongside standard client delivery. | 4.3 | Full comparison |
| Data Monsters | GPU-heavy deep learning, NVIDIA-partnered lab. | Elite NVIDIA partnership status supporting GPU-optimized deep learning delivery (per company website; independently unverifiable tier). | 4.2 | Full comparison |
| ITRex Group | Mid-market companies, AI/ML plus IoT/edge deployment. | Explicit focus on applied AI paired with intelligent-edge and IoT development, not just cloud-based ML. | 4.2 | Full comparison |
| Ideas2IT | Healthcare and BFSI enterprises, AI within product engineering. | Employee-ownership model paired with vertical focus in Healthcare, BFSI, and Manufacturing. | 4.1 | Full comparison |
| Quantiphi | Financial-services enterprises, cloud-native AI at scale. | AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing. | 4.4 | Full comparison |
| Fractal Analytics | Large enterprises, publicly-listed AI/analytics partner. | First Indian AI company to complete an IPO (NSE/BSE, February 2026), adding public financial transparency. | 4.4 | Full comparison |
| Tredence | Retail, CPG, industrials — vertical-focused data science at... | Deep vertical focus applying AI specifically within retail, CPG, and industrials contexts rather than horizontal AI consulting. | 4.2 | Full comparison |
| Sigmoid | Large enterprises, data-engineering-first ML delivery. | Data-engineering-first delivery model, with ML/AI built directly on pipelines the firm also builds and manages. | 4.2 | Full comparison |
| LatentView Analytics | Companies wanting BI delivery with ML layered in. | Publicly listed (NSE/BSE since 2021) analytics firm with two decades of operating history. | 3.9 | Full comparison |
| Indium Software | Existing Indium QA clients adding AI/ML. | Long-standing QA and testing heritage now paired with proprietary AI accelerators like teX.ai. | 3.8 | Full comparison |
| Grid Dynamics | Enterprises needing SEC-level transparency, AI at scale. | Nasdaq-listed public company (GDYN) with SEC-filed financials, offering procurement transparency few competitors match. | 4.1 | Full comparison |
| Persistent Systems | Very large enterprises, AI from their existing IT... | Enterprise-wide scale (24,000+ employees) supporting AI/ML as part of a full IT services portfolio, not a standalone specialty. | 3.8 | Full comparison |
| EPAM Systems | Largest global enterprises, AI within a massive engineering... | Largest headcount on this list (62,000+) with NYSE-listed financial transparency and a proprietary LLM orchestration platform (EPAM DIAL). | 3.8 | Full comparison |
| SoftServe | Enterprises wanting an established AI/ML, cloud, and IoT... | 32 years of continuous operation spanning both a US public-market presence and deep Ukrainian engineering roots. | 4.0 | Full comparison |
| N-iX | Fortune 500 clients, European-HQ dedicated ML/AI line. | 23 years of operating history originating from a Novell technology acquisition, now serving Fortune 500 clients from a Malta-based HQ. | 4.0 | Full comparison |
| DataArt | Finance, media, healthcare enterprises — established global AI... | 28 years of operating history across 30+ global delivery locations, with a newer (2024) dedicated AI strategy consulting service line. | 3.9 | Full comparison |
| Andersen | Mid-to-large enterprises, AI/ML plus custom software, one vendor. | Named AI-powered robotic integration line alongside standard AI/ML and data science services. | 4.0 | Full comparison |
| Innowise Group | Companies wanting AI/ML within full-cycle software development. | Full-cycle software development scope (web, mobile, cloud, QA, security) with AI/ML as one of several integrated specialties. | 3.9 | Full comparison |
| Sigma Software Group | Companies wanting ML from a top-ranked outsourcing firm. | Consecutive annual placement on IAOP's World's Top 100 Outsourcing list every year since 2015. | 4.0 | Full comparison |
| Exadel | Enterprises, end-to-end model design through MLOps. | Explicit end-to-end scope 'from model design to MLOps and integration' as one of five named core service lines. | 4.1 | Full comparison |
| MobiDev | Retail, hospitality, fitness companies — proven mid-size AI... | 65+ delivered AI/ML products concentrated in retail, hospitality, fitness, and health/wellness verticals. | 4.2 | Full comparison |
| Master of Code Global | Companies building conversational AI and chatbot products. | Specialization narrowly focused on conversational AI and chatbots, with 1,000+ projects delivered over 21 years. | 4.1 | Full comparison |
| ScienceSoft | Companies wanting AI/ML from an established IT generalist. | 36 years of continuous IT consulting history, one of the longest track records among firms on this list. | 3.9 | Full comparison |
| Intellectsoft | Enterprises wanting AI app development, brand-name client history. | Named enterprise client roster (EY, Harley-Davidson, London Stock Exchange, Qualcomm, Jaguar) rare among mid-size firms on this list. | 4.0 | Full comparison |
| Belitsoft | Small-to-mid companies, affordable AI/ML add-on. | 21 years as a custom software development firm now expanding deliberately into generative AI and predictive analytics. | 3.9 | Full comparison |
| Neoteric | SMBs wanting an accessible generative-AI specialist. | 20 years of operating history condensed into a compact, generative-AI-focused team rather than a broad IT services portfolio. | 4.3 | Full comparison |
| Addepto | Companies wanting boutique AI/BI, now KMS-backed. | Boutique AI/BI consultancy that gained additional scale and resources through its December 2025 acquisition by KMS Technology. | 4.1 | Full comparison |
| Softweb Solutions | Companies needing AI/ML plus IoT, Avnet-backed. | Backed by Avnet, a global electronics distributor, giving unusual hardware/IoT supply-chain proximity for AI-on-device projects. | 3.9 | Full comparison |
Provectus FAQ
What is Provectus?
Provectus is an AI and cloud engineering consultancy founded in 2010 by Stepan Pushkarev, headquartered in Palo Alto with 500–1,000 employees across roughly nine locations. The company positions itself as a mid-market AI-first systems integrator, combining big-data engineering, cloud engineering, and applied ML/AI practices, and holds partner status with major cloud providers (per company website; independently unverifiable exact partnership tier).
How much does Provectus charge?
Provectus uses fixed project and dedicated team engagements pricing. Minimum engagement starts at $50K. A discovery call is required to get project-specific quotes.
What tech stack does Provectus use?
Provectus works with Python, TensorFlow, PyTorch, AWS, Kubeflow, Apache Spark. Primary industries served include Retail, Healthcare, Financial Services, Technology/SaaS.
Is Provectus right for enterprise?
Mid-market and enterprise buyers, AI bundled with cloud engineering. 501–1,000 team size. Key consideration: Broader systems-integrator scope means ML-specialist depth is spread across cloud and data-engineering practices rather than singularly focused.
What are the best Provectus alternatives?
The best alternatives to Provectus depend on your use case. Top options are:
- Neurons Lab: one of the few ai consultancies worldwide holding aws's advanced machine learning consulting competence.
- Tensorway: full-stack ml delivery — data science, mlops, and llm/agentic frameworks (langchain, langgraph, autogen) — in one team.
- InData Labs: dedicated in-house r&d center focused specifically on data science and ai rather than broad software outsourcing.