For more than two decades, ScienceDocs has been a trusted strategic partner for researchers, universities, startups, and companies at the forefront of innovation. Since 2004, we have supported thousands of scientific projects and federal grant proposals—and in the era of AI and advanced computing, our expertise has expanded dramatically.

Today, ScienceDocs 3.0 brings together high-end AI grant writers, machine learning engineers, optimization engineers, data scientists, and computer science consultants with deep experience in both technical design and competitive federal funding. Our team supports innovations in AI/ML, data engineering, autonomous systems, computational science, and hybrid biology–AI applications.

With our startups as well as university clients increasingly working on cutting-edge, AI-driven technologies, our role is to help them articulate, justify, and optimize these innovations—technically, strategically, and competitively.  For example, watch one of our AI education webinars we delivered to the UC Davis Team in 2025 at the Dept of Land, Air and Water Resources for which we worked on this project.  We are perpetually open to both supportive AI consulting as well as select partnerships.

Core Technical Expertise

Our CS/ML and AI consulting team includes specialists in:

Machine Learning & AI Engineering

• Advanced model development using TensorFlow, PyTorch, JAX, and Scikit-learn

• Multi-modal models (vision, text, tabular, biosignal)

• Large-Language Model (LLM) fine-tuning and alignment

• Agentic AI systems, including:

  • tool-calling

  • retrieval-augmented generation (RAG)

  • chain-of-thought reasoning

  • multi-agent orchestration

• Explainable AI (XAI) and transparent model design

• Python, Cython, NumPy, SciPy, Pandas, CUDA-accelerated workflows

Reasoning, Optimization & Decision Science

• Decision-optimization algorithms (MILP, CP-SAT, RL, heuristics)

• Planning and scheduling systems

• Predictive modeling and forecasting

• Knowledge-graph–driven reasoning

• Autonomous agent planning for robotics, healthcare, and logistics

Data Engineering & Big Data

• Large-scale data processing using Spark, Hadoop, distributed pipelines

• Cloud data infrastructure (AWS, Azure, GCP)

• SQL expertise (PL/SQL, MySQL, SQL Server, PostgreSQL)

• Data ingestion, cleaning, fusion, and feature engineering at scale

Software & Full-Stack ML Development

• End-to-end ML pipelines (MLOps)

• Application development in Python, JavaScript, C++, and cloud-native stacks

• API development and cloud deployment using Docker, Kubernetes, Cloud Run

• Secure, compliance-oriented architectures for research environments

Interdisciplinary Proposal Development

The majority of SBIR/STTR projects since 2020 incorporate AI components. ScienceDocs is uniquely positioned to support these hybrid efforts.

A typical project may involve:

• AI and machine learning

• Molecular biology

• Biomedical engineering

• Robotics

• Chemistry

• Software engineering

• Data science

We assemble custom teams that combine domain-expert scientists with AI/ML specialists to ensure the proposal is both scientifically sound and technically compelling.

For example:
A proposal involving genomic sequencing and machine learning may pair a PhD molecular biologist as lead writer with a machine learning engineer who ensures the computational aspects are rigorous, defensible, and aligned with federal review expectations.

This interdisciplinary model is one of the reasons ScienceDocs maintains one of the strongest track records in SBIR/STTR proposal consulting.

Meet the Team

Below is a selection of our computer science, machine learning, and AI grant-writing consultants.
If you prefer a specific specialist, simply mention their name when submitting your quote request. Otherwise, we will match you with the ideal available expert based on your project’s technical needs.