Addepto has earned recognition as a data science and AI consulting firm, turning raw data into working AI-led proof-of-concepts for its clients.

Yet, when enterprises turn to Addepto for large-scale transformation, many find themselves facing the “prototype ceiling.” Firms are often faced with impressive demos that don’t always translate to production systems, limited integration with enterprise stacks, and rising costs when scaling. 

Gartner says that 40% of agentic AI projects will be cancelled by the end of 2027 owing to ‘escalating costs, unclear business value, or inadequate risk controls.’ A pain point that often surfaces in Addepto-style engagements. The same study says 80% of projects remain artisanal and never scale to reach industrialization. 

The challenges need to be addressed at the infra-level. 

That’s why more companies are evaluating Addepto alternatives, such as RTS Labs, LeewayHertz, The Hackett Group, and Cognizant, capable of marrying AI strategy with enterprise engineering. 

This guide compares these firms head-to-head and explains why RTS Labs leads the pack with flexible delivery models, faster execution, and measurable ROI.

What Is Addepto?

Addepto is a data science and AI consulting company founded in 2017 and based in Warsaw. It works across sectors such as finance, retail, manufacturing, and e-commerce, providing analytics and AI-related project support. 

The firm’s work typically involves building predictive models, automating selected processes, and setting up AI systems that align with a client’s existing analytics environment. 

Addepto is also involved in proof-of-concept projects that allow organizations to test AI use cases and check model behavior before wider deployment.

Services include:

• AI and machine-learning development for forecasting, customer analysis, and routine task automation
  
• Data infrastructure setup, including cloud pipelines and warehousing structures
  
• Analytics and BI work focused on dashboards and reporting tools
  
• Support for workflow automation
  

Pricing model:

Addepto follows a project-based structure, with costs shaped by scope, data complexity, and team size.

Why Look for Addepto Alternatives?

Addepto delivers capable machine learning and data science expertise, but many organizations outgrow its project-based consulting model as they scale their AI ambitions. Businesses that move from proof-of-concept to enterprise deployment often encounter key challenges:

• Scalability and integration limits: Solutions may not fit smoothly into multi-cloud or ERP ecosystems, which can restrict enterprise-wide adoption.
  
• Delivery speed and flexibility: Linear project timelines can slow down iteration and make cross-unit rollouts harder to manage.
  
• Pricing transparency concerns: The model can work for early experimentation, but long-term or enterprise programs may face higher, less predictable costs. Organizations that want clear ROI alignment or outcome-based pricing often compare alternatives.
  
• Narrow service scope: Companies needing broader transformation—covering AI, data engineering, cloud, and app modernization—may find the offering too limited.

Top Addepto Alternatives in 2026

With changing times, businesses are reevaluating their technology partners to find those capable of delivering enterprise-scale, compliant, and ROI-driven solutions. 

Addepto remains a strong contender in data science. But a new wave of consultancies, including RTS Labs, LeewayHertz, The Hackett Group, and Cognizant, now offer broader transformation capabilities that span strategy, data modernization, and automation.

1. RTS Labs – The Most Scalable Addepto Alternative for AI and Data Transformation

RTS Labs is an AI, data, and digital transformation consulting firm that helps organizations move from isolated analytics projects to enterprise-grade, production-ready AI ecosystems. RTS Labs builds custom, audit-ready AI solutions integrated directly with clients’ data infrastructure, ensuring scalability, governance, and measurable business impact.

RTS Labs is headquartered in Richmond, Virginia. It partners with mid-market and enterprise clients across finance, healthcare, SaaS, and manufacturing, delivering AI strategy, data modernization, and intelligent automation within 90-day agile sprints.

RTS Labs Key Features

• End-to-End AI Implementation: RTS Labs covers the entire lifecycle, starting from AI readiness assessment and use case discovery to model deployment, monitoring, and post-launch optimization so that every model delivers measurable ROI.
• Custom AI Agents & Predictive Systems: The firm builds audit-ready AI agents that automate forecasting, reconciliation, and risk detection. Each system is explainable, traceable, and compliant, designed to scale across ERP, CRM, and BI ecosystems.
• Data Engineering & Integration Expertise: RTS Labs’ data architecture teams modernize legacy pipelines into real-time data fabrics. This allows clients to unify siloed systems and operationalize analytics for decision velocity.
• Governance & Explainable AI (XAI): Governance is baked into design. Every RTS system includes decision-logging, bias monitoring, and role-based access controls (RBAC) aligned with SOX, GDPR, and ISO 27001 standards.
• Flexible Engagement Models: RTS offers co-creative, agile delivery, allowing mid-market clients to start small and scale incrementally without heavy retainer commitments.

Also read: RTS Labs AI Case Studies: How AI is Being Used in Finance

RTS Labs’ Pros and Cons

Pros

• Full-stack AI + data + software expertise under one roof.
  
• Tailored delivery model with measurable KPIs and post-launch ROI tracking.
  
• Strong governance, audit-readiness, and compliance integration.
  
• Proven ability to turn PoCs into production-scale systems.
  

Cons

• Custom builds take slightly longer than off-the-shelf automation tools.
  
• Requires client-side collaboration for data readiness and architecture setup.

RTS Labs Pricing

RTS Labs uses a custom, outcome-based pricing model aligned with project maturity and ROI goals. The team typically starts engagement with a diagnostic workshop to scope opportunities and deliverables. They then follow up by modular pricing tied to business outcomes.

Why RTS Labs Outperforms Addepto

For enterprises seeking agility, compliance, and measurable ROI, RTS Labs is the most complete Addepto alternative in 2026.

1. RTS Labs Delivers at Scale From PoC to Production

Where Addepto often focuses on proof-of-concept development, RTS Labs takes organizations all the way to production-grade AI. Every project is engineered for real-world deployment with model governance, monitoring, and continuous learning built in.

2. Integration Without Barriers via Unified Data Ecosystems

Addepto’s solutions can function as stand-alone builds. RTS Labs, by contrast, integrates seamlessly across ERP, CRM, cloud, and data warehouse systems, creating a single source of financial and operational truth.

3. Faster Time-to-Value Through Agile Co-Creation

Instead of rigid project sprints, RTS Labs works in agile, embedded teams that deliver measurable outcomes every few weeks. Clients stay involved throughout. A model that halves typical enterprise deployment timelines.

4. Enterprise-Grade Governance and Audit-Ready AI

RTS Labs builds trust into automation. Every AI model features decision logging, bias monitoring, and explainable reasoning, ensuring compliance with SOX, GDPR, and ISO 27001 standards. 

Addepto opts for project-level compliance, but RTS Labs embeds governance at the system design level, which is essential for regulated sectors like finance and healthcare.

5. Flexible, Outcome-Based Pricing Aligned to ROI

Instead of hourly or milestone billing, RTS Labs offers modular, results-linked pricing, ensuring clients pay for business impact, not effort. This model empowers mid-market firms to access enterprise-grade AI without restrictive retainers or hidden costs.

Here’s a head-to-head comparative analysis between Addepto and RTS Labs: 

Criteria	RTS Labs	Addepto
Scope of Services	Full-stack AI, data engineering, digital transformation, and app modernization	Primarily AI & ML consulting with data engineering support
Delivery Model	Agile, co-creative teams delivering ROI within 90 days	Linear, project-based engagements focused on PoCs
Integration Capability	Cross-platform integration across ERP, CRM, and cloud systems	Limited to single-solution or standalone AI deployments
Governance & Compliance	Built-in explainability, bias monitoring, audit trails, and RBAC	Basic compliance reviews; no embedded governance
Scalability & Maintenance	Enterprise-grade architecture with continuous optimization and retraining	Project-level models that require re-engagement for scaling
Speed to Value	40–50% faster implementation cycles via agile sprints	Slower transition from pilot to production
Pricing Transparency	Outcome-based, modular pricing tied to measurable ROI	Hourly/milestone billing with variable costs at scale
Best Fit For	Mid-market and enterprise firms ready to scale AI responsibly	Startups or organizations exploring AI at early stages

 2. LeewayHertz

LeewayHertz is a U.S.-based technology consultancy known for emerging tech innovation. It develops AI, blockchain, and IoT solutions tailored for startups and enterprises.

LeewayHertz’s Key Features

• AI & blockchain development
  
• Cloud-native data platforms
  
• IoT and edge intelligence
  
• Rapid MVP and prototype delivery
  
• Full-stack software engineering

LeewayHertz’s Pros and Cons

Pros

• Excellent for quick MVPs
• Strong in emerging tech integration

Cons

• Limited enterprise-scale delivery
• Less focus on audit-ready AI

LeewayHertz’s Pricing

It offers transparent MVP-based pricing. Enterprise projects are quote-based.

3. The Hackett Group

Hackett Group is a global advisory and benchmarking firm specializing in digital transformation and finance optimization.

Hackett Group’s Key Features

• Enterprise process benchmarking
  
• Finance transformation consulting
  
• Analytics and performance improvement
  
• Digital operating model design
  
• Cloud ERP enablement

Hackett Group’s Pros and Cons

Pros

• Brings strong process optimization expertise for complex enterprise environments
  
• Widely trusted by Fortune 500 companies for finance and operations transformation

Cons:

• Engagements lean heavily toward advisory rather than hands-on execution
  
• Implementation cycles tend to be slower compared to agile boutique firms

Hackett Group’s Pricing

Premium consulting retainers based on enterprise scope.

4. Cognizant

Cognizant is a multinational IT and consulting giant offering enterprise AI, data modernization, and managed services across multiple industries.

Cognizant’s Key Features

• AI and automation strategy
  
• Cloud data modernization
  
• Industry-specific AI accelerators
  
• Managed service delivery
  
• End-to-end enterprise integration

Cognizant’s Pros and Cons

Pros

• Offers large-scale, reliable delivery for complex enterprise transformation
  
• Provides deep industry expertise across BFSI, healthcare, retail, and manufacturing
  
• Supports global programs with extensive engineering and managed service teams
  

Cons:

• Engagement structures can be complex and harder to navigate for newer clients
  
• Response times and agility are slower for mid-market organizations needing fast iteration

Cognizant’s Pricing

It has an enterprise pricing model and typically requires long-term contracts.

5. Slalom 

Slalom pairs regional consulting teams with cloud vendor partnerships to deliver cloud migrations, data modernization, and change management for mid-to-large organizations. Good for companies that value close client relationships and hands-on delivery.

Slalom’s Key features

• Cloud migration & modernization
  
• Data platform design and analytics enablement
  
• Change management & adoption programs
  
• Custom software and integration delivery
  
• Partnerships with AWS, Azure, Google Cloud

Slalom’s Pros and Cons 

Pros:

• Builds strong, collaborative client relationships through its regional consulting model
  
• Maintains deep partnerships with AWS, Azure, and Google Cloud for modernization projects

Cons:

• Regional, people-heavy delivery structure can slow down large-scale execution
  
• Engagements often cost more than boutique firms offering similar technical capabilities
  

Slalom’s Pricing

Pricing structures are quote-based, typically project or retainer models.

6. InData Labs 

InData Labs focuses on machine learning, computer vision, and advanced analytics, delivering strong predictive models for retail, logistics, and healthcare use cases.

InData Labs’ Key features

• Predictive modeling & computer vision
  
• Data annotation and model training pipelines
  
• MLops and model monitoring support
  
• Business intelligence and visualization
  
• Domain-specific modeling templates

InData Labs’ Pros and Cons

 Pros:

• Offers strong machine learning expertise with highly tailored predictive models
  
• Delivers specialized analytics solutions suited for data-intensive industries
  

Cons:

• Provides limited transformation breadth beyond core ML capabilities
  
• Lacks full-stack integration and governance features needed for enterprise-scale deployments

InData Labs’ Pricing

It offers project-based engagements, and its pricing varies by model complexity.

7. Lean Layer 

Lean Layer serves product teams and SaaS companies with lightweight, fast AI and data delivery designed for rapid iterations and cost control.

Lean Layer’s Key features

• MVP and incremental AI delivery
  
• Lightweight data pipelines & analytics
  
• Product-led integration with SaaS stacks
  
• Fast prototyping and A/B testing support
  
• Developer-friendly toolchains and CI/CD for models

Lean Layer’s Pros and Cons

Pros:

• Delivers fast time-to-value with lightweight, iterative AI development
  
• Offers predictable MVP-focused pricing ideal for SaaS and product teams
  

Cons:

• Lacks the governance depth required for highly regulated or enterprise environments
  
• Not designed for complex multi-ERP or large-scale integration needs
  

Lean Layer’s Pricing

It offers transparent MVP tiers or subscription pricing.

8. Skaled

Skaled focuses on GTM strategy and revenue operations, using data and automation to improve sales efficiency, forecasting, and pipeline execution for B2B/SaaS firms.

Skaled’s Key features

• Revenue operations and lead orchestration
  
• Sales process design and enablement
  
• Predictive lead scoring and forecasting aids
  
• CRM integrations and dashboarding
  
• GTM playbooks and coaching services

Skaled’s Pros and Cons

Pros:

• Drives quick, measurable improvements in sales performance and GTM execution
  
• Prioritizes revenue impact with targeted process and automation enhancements

Cons:

• Focuses primarily on GTM and sales operations, limiting broader transformation scope
  
• Not equipped to deliver full-stack AI, data engineering, or enterprise integration projects

Skaled’s Pricing

It offers package and retainer models geared to revenue outcomes.

9. N-iX 

N-iX offers dedicated engineering teams that build custom data platforms, AI models, and enterprise software. It is a solid option for organizations needing sustained engineering capacity.

N-iX’s Key features

• Dedicated engineering squads & nearshore teams
  
• Custom data platforms and ML engineering
  
• Integration with enterprise systems and APIs
  
• Scalable product engineering and SRE services
  
• Domain experience in fintech, healthcare, and manufacturing

N-iX’s Pros and Cons 

Pros:

• Provides strong engineering capacity with flexible, long-term team augmentation models
  
• Scales custom AI and software development efficiently across large or ongoing projects
  

Cons:

• Requires clients to define more of the governance and compliance framework themselves
  
• Offers fewer built-in audit, explainability, and oversight features compared to enterprise-focused consultancies
  

N-iX’s Pricing

Team-based / time & materials contracts; flexible long-term engagement pricing.

Consulting Fit Matrix: Addepto vs Top Alternatives in 2026

Here’s a quick snapshot of how RTS Labs compares to other leading consulting firms to help decision-makers quickly identify which partner aligns best with their company’s data maturity, scalability needs, and digital transformation goals.

Criteria	RTS Labs	LeewayHertz	The Hackett Group	Slalom	Cognizant	InData Labs	Lean Layer	Skaled	N-iX
Ideal Client Size	Mid-market to Enterprise ($100M–$4B)	Startups to Enterprise	Large Enterprise	Mid to Enterprise	Enterprise	Mid-market	Mid-market	Mid-market	Mid to Enterprise
Engagement Model	Co-creative, agile, embedded teams	Product + consulting hybrid	Advisory-led	Regional, collaborative	Managed global delivery	Project-based	Agile consulting	GTM & Sales enablement	Dedicated engineering teams
Core Strength	AI strategy + data modernization	AI & emerging tech	Finance & process optimization	Data + cloud modernization	Enterprise modernization	Predictive analytics	Lean AI delivery	Sales ops automation	Software + AI delivery
Industry Focus	Finance, Healthcare, Construction, SaaS	Tech, Startups, IoT	Finance, HR, Ops	Healthcare, Retail, Public Sector	BFSI, Manufacturing, Healthcare	Retail, Logistics, Healthcare	SaaS, Product-led firms	SaaS, B2B	Fintech, Healthcare, Retail
Speed to Value	Fast (Agile + outcome-driven)	Fast (MVP-first)	Moderate	Moderate	Moderate	Moderate	Fast	Fast	Fast
Pricing Flexibility	High – outcome-based and scalable	High–transparent MVP pricing	Low – retainer model	Medium	Low	Medium	High	Medium	High
AI Implementation Depth	End-to-end (strategy → data → deployment)	Pilot-to-MVP level	Advisory	Implementation-heavy	Scaled enterprise AI	ML-heavy	Moderate	Limited	Custom builds
Digital Transformation Breadth	Broad (AI, data, apps, cloud)	Medium	Broad (ERP, finance, HR)	Broad	Broadest	Narrow	Medium	Narrow	Broad
Compliance & Governance	Built-in explainability, audit logs, and RBAC	Basic model documentation	Advisory-level controls	Regional compliance support	Enterprise-grade frameworks	Limited	Basic	Limited	ISO & SOC2-ready
Best Fit For	Firms seeking AI-led transformation with ROI focus	Tech startups, innovators	Fortune 500 optimization	Mid-size firms needing data strategy	Global corporations	Firms needing ML models	SaaS & lean teams	GTM-led orgs	Companies scaling custom AI engineering

How to Choose the Right Addepto Alternative

Selecting the right Addepto alternative depends on your organization’s AI maturity, data infrastructure, and growth goals. As more firms move beyond proof-of-concept AI into production systems, the ideal partner must combine technical depth, business alignment, and measurable ROI.

Here are five key factors to guide your choice:

• Assess Data Maturity and Integration Needs: Evaluate whether the firm can connect AI with your existing ERP, CRM, and data lakes to avoid siloed outcomes.
  
• Prioritize Scalability and Governance: Look for partners that embed explainability, compliance, and role-based access into every AI deployment.
  
• Evaluate Speed-to-Value: Agile, co-creative models deliver faster results than linear project engagements.
  
• Seek Industry Alignment: Industry-aware partners understand your KPIs, regulatory landscape, and workflows.
  
• Ensure Transparent, Outcome-Based Pricing: The right firm links costs directly to business results, not hourly estimates.

The Verdict: Choosing Scalable AI Starts with the Right Partner

The shift from experimenting with AI to scaling it enterprise-wide requires more than a capable vendor. It demands a transformation partner who can blend speed, governance, and measurable outcomes.

While firms like LeewayHertz, The Hackett Group, and Cognizant deliver credible solutions in their domains, RTS Labs brings boutique agility and enterprise sophistication. Its co-creative approach, audit-ready AI architecture, and cross-platform integration capabilities make it the most future-proof Addepto alternative for organizations ready to operationalize intelligence.

With a track record of driving faster close cycles, smarter forecasting, and improved data-driven decision speed, RTS Labs helps businesses unlock the full ROI potential of their AI investments. 

Explore the full portfolio of success stories here → RTS Labs Case Studies
Or schedule a discovery session to discuss your AI roadmap → Contact RTS Labs

FAQs

1. What should I look for when shortlisting Addepto competitors?

Focus on end-to-end execution capability, not just data science skills. The best alternatives combine AI strategy, integration engineering, and governance. This ensures AI models don’t just work in isolation but deliver measurable business outcomes across systems.

2. How fast can AI projects move from pilot to production with the right partner?

Firms using agile co-creation models typically achieve deployment in 8–12 weeks for mid-scale projects. RTS Labs’ iterative delivery approach allows clients to see live results faster without waiting for full program completion.

3. Do all Addepto alternatives offer compliance and audit-ready AI?

No. While most provide security best practices, few integrate explainability, bias monitoring, and decision logging at the model level. RTS Labs builds these controls into every engagement, making its AI systems audit-ready by design, an edge for regulated industries.

4. How does pricing vary among Addepto competitors?

Pricing structures range from hourly consulting (Addepto) to fixed MVP packages (LeewayHertz) to outcome-based models (RTS Labs). The latter aligns directly with ROI metrics, offering transparency and scalability as project scope evolves.

5. Why is RTS Labs considered the best long-term Addepto alternative?

RTS Labs goes beyond short-term AI delivery. It helps enterprises build a sustainable AI foundation through modern data architecture, continuous optimization, and embedded governance. This makes it not just a service provider, but a transformation partner for the AI-driven decade ahead.