AI and ML engineering services that ships to production.

Our AI and ML engineers build models, train systems, integrate LLMs into products, and deploy ML to production. Our data analysts build dashboards, automate reporting, and analyze business data using BI tools and SQL. Most companies need a data analyst before they need an ML engineer. If you’re not sure which fits, we can scope it on the initial call.

Yes. LLM application development is one of the most common engagement types right now. Our engineers build RAG systems, chatbots, agents, and AI-native product features on GPT, Claude, Gemini, or open-source models. We work in LangChain, LlamaIndex, or your preferred orchestration framework.

Yes. POC-to-production is where most AI projects stall, and where our engineers add the most value. We handle the engineering work that prototypes skip: error handling, cost monitoring, latency optimization, evaluation frameworks, and the MLOps infrastructure that keeps models healthy in production.

We place mid-level engineers (3-5 years), senior engineers (5-8 years), and staff-level ML engineers (8+ years). For most AI/ML work, senior is the right fit given the engineering complexity. For research-heavy or novel architecture work, staff-level talent fits better.

Both. Our engineers fine-tune open-source models (Llama, Mistral, custom architectures) and integrate commercial APIs (OpenAI, Anthropic, Google). The right approach depends on your use case, data, cost constraints, and IP requirements. We match engineers with experience across both paths.

Yes. We have engineers experienced with AEC firms on document AI (RFPs, specs, contracts), generative design support, computer vision for construction site monitoring and safety, automated drawing review, and BIM data analysis with ML. If your firm is exploring AI in design or operations, we match engineers with relevant context.

Your engineer accesses your data through your standard provisioning and security protocols. BetterPros does not store your training data, models, or proprietary code on our infrastructure. For engagements with regulated data (HIPAA, financial, federal), we confirm device and access setup during onboarding.

Yes. Most engagements involve significant data engineering: pipelines, feature engineering, and quality assurance. Many of our ML engineers also have data engineering experience. For dedicated data engineering work, we can place engineers focused entirely on that.

Yes. Most engagements start with one engineer focused on a specific use case (LLM app, model training, deployment) and grow from there. No minimum team sizes, no long-term commitments.

We replace them at no additional cost. AI and ML fit is specific: your stack, your use case, your data, your code style. If the match isn’t right after onboarding, we find someone who fits better.