ScienceSoft
35-year-old enterprise software firm with a mature ML consulting and development practice.
Founded 1989 | McKinney, TX, USA | 700+ employees
custom-mlml-consultingpredictive-analyticsdata-engineeringnlp
What is ScienceSoft?
ScienceSoft is a US-based IT consulting and software development company founded in 1989 and headquartered in McKinney, Texas. The company employs 700+ professionals and has been delivering enterprise software for 35+ years, with an ML practice serving healthcare, retail, financial services, manufacturing, and government clients. ScienceSoft's unusual organizational longevity provides compliance readiness, institutional knowledge, and process maturity rare in younger ML-focused firms.
ScienceSoft was founded in 1989 and is headquartered in McKinney, TX, USA. The firm employs 700+ people and works primarily with clients in healthcare, retail, financial services, manufacturing, government sectors. Its primary differentiator is: 35+ years of enterprise delivery experience with a mature ML practice — providing compliance readiness, institutional knowledge, and process maturity rare in younger ML-focused competitors.
ScienceSoft tech stack and services
PythonRTensorFlowScikit-LearnAzure MLAWS SageMakerSQL ServerPower BIApache SparkMLflow
| Service area |
|---|
| Custom ML Development |
| ML Consulting |
| Predictive Analytics |
| Data Engineering |
| NLP |
ScienceSoft use cases
Short answer: ScienceSoft is best suited for established enterprises, 35+ years, stable US vendor.
| Use case |
|---|
| ML consulting and roadmap development for enterprises beginning their AI programme |
| Predictive maintenance model development for manufacturing equipment |
| Healthcare ML development for clinical data analysis and patient outcome prediction |
| Government-sector ML projects requiring compliance and data sovereignty controls |
| NLP development for document automation in financial services or legal operations |
ScienceSoft pricing
Short answer: ScienceSoft uses a fixed project, t&m, dedicated team, retainer pricing approach. Minimum engagement starts at $30K.
| Engagement model | Typical range | Best for |
|---|---|---|
| Fixed project | From $30K | Well-defined scope |
| T&M | Variable; depends on team size | Large programmes or team augmentation |
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
| Retainer | Monthly rate; not public | Ongoing AI engineering |
ScienceSoft does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
ScienceSoft pros and cons
| Advantages | Things to consider |
|---|---|
| +35+ years of enterprise software delivery history gives clients a stable long-term partner | -Generalist heritage means ML is one of many practice areas — less specialist depth than pure-play boutiques |
| +US-based HQ with government sector experience including compliance-aware ML delivery | -Less exposure to cutting-edge LLM and generative AI tooling than newer AI-native firms |
| +Retainer model available for ongoing ML improvement and model maintenance programmes | -Larger organization may mean slower engagement initiation than boutiques |
| +Broad technology coverage across Python, R, Azure ML, and AWS SageMaker | |
| +Established reputation on Clutch and industry directories with long-standing client relationships |
ScienceSoft vs alternatives
How ScienceSoft compares to the other top Machine Learning Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Dedicated ML boutique, strategy through production MLOps. | ML-only focus with a dedicated specialist team backed by 25 years of the parent company software delivery infrastructure — unusually deep for a firm of this size | 4.8 | Full comparison |
| LeewayHertz | Enterprises, end-to-end AI delivery, Fortune 500 clients. | Product-centric AI delivery culture with verified Fortune 500 client references including ESPN, Siemens, and 3M — now operating within The Hackett Group | 4.0 | Full comparison |
| InData Labs | Mid-market orgs, complex ML, deep data-science expertise. | Pure-play ML boutique with a measurably higher specialist-to-generalist ratio than typical service firms, confirmed by Clutch as a top AI service provider | 4.5 | Full comparison |
| HatchWorks AI | Companies wanting AI-native, generative-AI-embedded delivery. | Clutch #1 AI Services Company with a proprietary Generative Driven Development methodology claimed to reduce delivery time by 30–50% (per company website; independently unverifiable) | 4.4 | Full comparison |
| STX Next | Orgs needing ML operationalized in Python-native systems. | Europe's largest Python-specialist firm uniquely positioned to embed ML into production software without the integration friction that plagues pure-play ML boutiques | 4.3 | Full comparison |
| Tredence | Enterprises, last-mile ML adoption, supply chain and retail. | Industry-specific AI accelerators and a proven focus on last-mile ML adoption, closing the execution gap between data science output and real business value | 4.3 | Full comparison |
| Addepto | Finance, energy, retail — bespoke ML with pipeline... | End-to-end AI/ML delivery with particular sector depth in financial services and energy — industries that require compliance sophistication alongside technical capability | 4.2 | Full comparison |
| DataForest | Data-first companies, robust engineering foundation for ML. | Data engineering-first approach builds pipeline and data quality foundations before model development, addressing the root cause of most ML project failures | 4.2 | Full comparison |
| Forte Group | Orgs wanting Tier-1 rigor, specialist agility, roadmap to... | Structured AI service lines with Tier 1 delivery rigor and specialist consultancy agility — serving organizations that need both without enterprise-tier pricing | 4.1 | Full comparison |
| Binariks | Healthcare, fintech, insurance — compliance-first ML engineering. | Compliance-first ML engineering for regulated industries — governance and audit trails are built in from the architecture stage, not retrofitted after launch | 4.1 | Full comparison |
| Softeq | Hardware and industrial companies, edge ML on embedded... | Unique capability to combine hardware design expertise with ML engineering, deploying models at the edge where cloud-only ML firms cannot operate | 4.1 | Full comparison |
| Markovate | Retail, travel, fitness — recommendation engines, 300+ projects. | 300+ delivered projects spanning recommendation systems, computer vision, and dynamic pricing, with deeper consumer-facing ML specialization than most comparably sized firms | 4.0 | Full comparison |
| Miquido | Product teams, ML in polished products, Google-certified. | Google-certified AI/ML capability paired with strong product design — clients receive ML that works inside well-crafted user experiences, not bolted-on algorithms | 4.0 | Full comparison |
| Simform | Industrial and enterprise, cloud-native AWS ML at scale. | AWS Premier Partner with 1,000+ engineers and documented depth in industrial IoT ML — connecting physical sensor streams to cloud ML inference at production scale | 3.9 | Full comparison |
| Intuz | SMBs, fixed-price discovery, 1,700+ projects. | 1,700+ project track record with a discovery-first engagement model making enterprise-grade ML accessible to SMBs through risk-reduced fixed-price POC phases | 3.9 | Full comparison |
| Scopic | Orgs wanting custom ML, 20+ years, strong computer... | 20+ years as a distributed software company gives Scopic strong custom ML engineering discipline with confirmed production deployments across transportation and healthcare | 3.9 | Full comparison |
| N-iX | Enterprises, large-scale Eastern-Europe ML engineering capacity. | 2,400+ engineers with deep specialization in scalable AI architectures, able to field large dedicated teams for complex multi-year ML programmes at competitive Eastern European rates | 3.9 | Full comparison |
| Oxagile | Media, AdTech, sports — video ML, 20+ years... | 20+ years of video domain expertise uniquely positions Oxagile for ML use cases involving video understanding, visual search, and real-time video analytics | 3.8 | Full comparison |
| Innowise | Banking, agriculture, healthcare — compliance-aware ML. | Cross-vertical ML delivery with documented case studies in banking automation, agricultural forecasting, and healthcare diagnostics — unusual breadth across regulated industries | 3.9 | Full comparison |
| Intellectsoft | Fintech, healthcare, construction — ML in enterprise ecosystems. | Palo Alto HQ with 10 global delivery offices combining US-based account management with competitive Eastern European delivery rates for enterprise ML programmes | 3.8 | Full comparison |
| DataRoot Labs | Startups and scale-ups, AI strategy plus execution, accessible. | One of Ukraine's most recognized ML consultancies — combining strategy-level AI advisory with hands-on engineering, a combination rare at this team size and price point | 3.8 | Full comparison |
| Itransition | Enterprises, ML in legacy systems, 25+ years delivery. | 25+ years of enterprise software delivery with five dedicated R&D labs, giving clients a mature delivery operation with advanced ML research support at competitive rates | 3.9 | Full comparison |
| 10Pearls | US enterprises and government contractors, AI-native, LATAM delivery. | AI-native engineering culture with four CRN Solution Provider 500 recognitions and 1,400+ experts spanning North America and LATAM for enterprise AI programmes | 3.8 | Full comparison |
| Coherent Solutions | Microsoft-stack enterprises, #1 Twin Cities IT firm. | Ranked #1 IT consulting firm in the Twin Cities five times in six years with 2,000+ engineers across 10 development centers, offering enterprise ML at competitive rates | 3.8 | Full comparison |
| Iflexion | US orgs, ML within custom enterprise software systems. | 25 years of enterprise software delivery with 850+ professionals embedding ML into complete systems rather than delivering standalone models that require separate integration work | 3.7 | Full comparison |
| Appinventiv | Global businesses, mobile-first ML, five-continent delivery. | 1,600+ specialists with a mobile-first AI approach and global footprint delivering 1,000+ digital assets with embedded ML — strong for consumer-facing AI product work | 3.8 | Full comparison |
| Avenga | Telco, banking, automotive enterprises — 6,000+ engineer scale. | 6,000+ specialists across 44 delivery centers formed through PE-backed acquisitions, providing enterprise-scale AI delivery capacity — though cultural integration across legacy entities is ongoing | 3.7 | Full comparison |
| BairesDev | Companies wanting rapid ML scale-up, LATAM nearshore, US... | 4,000+ ML-capable LATAM engineers in US time zones with 1,200+ completed projects, enabling rapid scale-up for organizations that need to grow their ML capacity fast | 3.7 | Full comparison |
| Turing | Teams needing pre-vetted senior ML developers, staff augmentation. | AI-powered vetting platform screening 3M+ global ML developers to place the top 1% directly in client engineering teams at rates competitive with US in-house hiring | 3.7 | Full comparison |
| EPAM Systems | Large enterprises, Fortune 500 scale, global compliance. | 62,000+ engineers across 50+ countries delivering ML inside a full-service technology engineering operation — unmatched scale and compliance depth for global enterprise AI programmes | 3.9 | Full comparison |
ScienceSoft FAQ
What is ScienceSoft?
ScienceSoft is a US-based IT consulting and software development company founded in 1989 and headquartered in McKinney, Texas. The company employs 700+ professionals and has been delivering enterprise software for 35+ years, with an ML practice serving healthcare, retail, financial services, manufacturing, and government clients. ScienceSoft's unusual organizational longevity provides compliance readiness, institutional knowledge, and process maturity rare in younger ML-focused firms.
How much does ScienceSoft charge?
ScienceSoft uses fixed project, t&m, dedicated team, retainer pricing. Minimum engagement starts at $30K. A discovery call is required to get project-specific quotes.
What tech stack does ScienceSoft use?
ScienceSoft works with Python, R, TensorFlow, Scikit-Learn, Azure ML, AWS SageMaker, SQL Server, Power BI, Apache Spark, MLflow. Primary industries served include healthcare, retail, financial services, manufacturing, government.
Is ScienceSoft right for enterprise?
Established enterprises, 35+ years, stable US vendor. 700+ team size. Key consideration: Generalist heritage means ML is one of many practice areas — less specialist depth than pure-play boutiques.
What are the best ScienceSoft alternatives?
The best alternatives to ScienceSoft depend on your use case. Top options are:
- Tensorway: ml-only focus with a dedicated specialist team backed by 25 years of the parent company software delivery infrastructure — unusually deep for a firm of this size
- LeewayHertz: product-centric ai delivery culture with verified fortune 500 client references including espn, siemens, and 3m — now operating within the hackett group
- InData Labs: pure-play ml boutique with a measurably higher specialist-to-generalist ratio than typical service firms, confirmed by clutch as a top ai service provider
Compare ScienceSoft with other Machine Learning Development companies
ScienceSoft vs Tensorway ScienceSoft vs LeewayHertz ScienceSoft vs InData Labs ScienceSoft vs HatchWorks AI ScienceSoft vs STX Next ScienceSoft vs Tredence ScienceSoft vs Addepto ScienceSoft vs DataForest ScienceSoft vs Forte Group ScienceSoft vs Binariks ScienceSoft vs Softeq ScienceSoft vs Markovate ScienceSoft vs Miquido ScienceSoft vs Simform ScienceSoft vs Intuz ScienceSoft vs Scopic ScienceSoft vs N-iX ScienceSoft vs Oxagile ScienceSoft vs Innowise ScienceSoft vs Intellectsoft ScienceSoft vs DataRoot Labs ScienceSoft vs Itransition ScienceSoft vs 10Pearls ScienceSoft vs Coherent Solutions ScienceSoft vs Iflexion ScienceSoft vs Appinventiv ScienceSoft vs Avenga ScienceSoft vs BairesDev ScienceSoft vs Turing ScienceSoft vs EPAM Systems