Machine learning and generative AI development for real workflows, not demos

Most AI projects fail before the model gets a vote

MIT's NANDA initiative measured what operators already suspected: roughly 95% of enterprise GenAI pilots show no measurable P&L impact, and the buy-vs-build split is stark, externally partnered AI builds reach production about 67% of the time, internal builds about 33% (MIT NANDA, State of AI in Business). The gap is not model quality. It is unvalidated use cases, unready data, and no evaluation loop between demo and deployment.

Entropy & Co. provides machine learning and generative AI development for teams that want to land in the working minority. We build ML and AI systems that connect to business data, support real users, and improve measurable workflows, with use-case validation, data readiness, evaluation, and safe deployment treated as the actual work, not the afterthought.

One boundary up front: if what you need is workflow automation for business operations, lead follow-up, CRM syncing, reporting, onboarding, that has its own page: AI automation. This page covers the model and data engineering side: machine learning development, RAG, NLP, predictive analytics, and LLM systems built into products.

What we build

• RAG and knowledge-base assistants. Retrieval over your documents and databases, chunking, embeddings, pgvector or a managed vector store, grounded answers with source citations, and an evaluation set that catches regressions before your users do.

• Machine-learning models. Forecasting, scoring, and classification built with scikit-learn, XGBoost, or PyTorch, trained on your historical data, validated against holdout sets, deployed behind a FastAPI service.

• Predictive analytics. Churn risk, demand forecasting, lead scoring, anomaly detection, models that output a number someone can act on, delivered into the CRM or dashboard where the decision happens.

• NLP workflows. Document extraction, classification, summarization, and entity recognition across contracts, intake forms, support tickets, and call transcripts.

• Custom AI applications and copilots. LLM-powered features built on Claude, GPT, or Gemini APIs behind a model adapter layer, so a provider swap is a configuration change rather than a rewrite.

• AI agent engineering. Custom multi-agent systems with tool use, memory, retrieval, and orchestration, the scope that begins where off-the-shelf automation platforms run out of road. Wiring agents into your stack is its own problem; see how to integrate AI agents with existing systems.

• AI integrations into existing products. AI search, recommendations, scoring, or assistant features added to software you already operate.

• Evaluation, monitoring, and improvement loops. Tracing with Langfuse, versioned evaluation sets, drift detection, and cost monitoring, the unglamorous layer that decides whether the system survives quarter two.

Where these systems earn their keep

The use cases that repeatedly justify the engineering:

• Internal knowledge assistants over policies, contracts, and project history

• Lead scoring and qualification tied to actual close rates

• Forecasting and anomaly detection on revenue, inventory, or operations data

• Document intake and classification where volume buries a human team

• Customer support assistants grounded in your real documentation

• Sales enablement copilots that draft from CRM context

• Reporting summaries with next-action recommendations

The pattern across all of them: a measurable workflow existed first, and the model made it faster, cheaper, or more accurate by an auditable number.

How we work

1. Validate the use case. We ask whether AI is actually the right solution and what business outcome the system needs to support. A surprising share of "AI projects" are deterministic logic wearing a costume, agents vs automation covers how we draw that line.

2. Assess the data. We review available documents, databases, workflows, permissions, and quality issues. Most ML timelines die here, so we look here first.

3. Prototype the approach. We test the model, prompts, retrieval, user experience, and integration path before overbuilding. The prototype runs against a scored evaluation set, not a demo script.

4. Build for production. Authentication, logging, evaluation, fallbacks, cost controls, and human review where needed. Human-in-the-loop patterns are a default on sensitive actions, not an upsell.

5. Monitor and improve. AI systems need ongoing evaluation. We watch accuracy, usage, failure cases, cost, and user feedback, from a monitoring stack that lives in your accounts.

Buy, build, or don't build: we answer that first

The NANDA numbers argue for honesty before engineering: purchased and partnered tools succeed at roughly twice the rate of internal builds, and most failed pilots were the wrong projects, started anyway. Our scoping call ends with a named recommendation, build, buy, or don't build, including the product to buy when building is the wrong call. The buy-vs-build framework we use is public.

The same honesty applies inside the build. Fine-tuning is rarely the first answer, retrieval and prompt design solve most knowledge problems at a fraction of the cost, and a fine-tune only earns its keep once an evaluation set proves the cheaper approach has plateaued. A vendor proposing a custom model before showing an evaluation method is a red flag worth reading about.

Built for the rollback statistics

Sinch surveyed 2,500+ enterprise leaders and found 75% had rolled back at least one customer-facing AI agent. The causes were not model quality: data exposure (31%), hallucinated answers (22%), and no diagnostics to determine what went wrong (16%). The detail worth sitting with: the organizations Sinch rated most governance-mature rolled back more often than average. Governance surfaces failures, prevention is engineering done before launch.

That engineering ships as defaults in our builds: scoped data access (what agent permissions should look like), per-action audit logging, evaluation sets frozen before launch, staged rollout with written rollback triggers (the rollout plan we use), and human approval on consequential actions. The checklist lives in our AI agent governance framework.

What this work costs in the market

These are market anchors with sources, not our rates, your quote is built against your scope:

• A production AI agent runs $200–$4,000/month to operate, plus a one-time build between $0 and $40,000 depending on integrations, compliance, and evaluation depth, the full line-item breakdown is here

• Expert-supervised AI product builds land at $25K–$75K, shipped in days to three weeks, against $75K–$500K+ over multiple quarters at traditional agencies (Chrono, 2026); the AI MVP cost math itemizes it

• Mid-tier model APIs run roughly $1–$5 per million input tokens, so per-workflow cost is forecastable before you build

What moves a number inside those bands: integration count, data cleanup volume, compliance requirements (HIPAA and SOC 2 add real work), evaluation depth, and who maintains the system after day 30. Bring a scope and you get a number against it, explore an AI use case.

Results we can point to

Entropy's case studies show automation and AI impact across order fulfillment, legal operations, lead intake, CRM syncing, and client onboarding, alongside an 18x ROAS campaign for a premium leather goods manufacturer and a DTC turnaround from negative ROAS to consistent profitable acquisition. The lesson is consistent: these systems work when they are tied to a measurable workflow, not a novelty demo. The same rule governs our ML work, a model that cannot show its lift against a baseline does not ship.

FAQ

Do we need a large dataset?

Not always. Some AI systems use your existing documents and tools through retrieval. Classic ML models usually require more structured historical data, exactly what step two of our process checks before anyone commits to a build.

Can you build AI into an existing product?

Yes. We can add AI search, assistants, scoring, content workflows, recommendations, or automation features to existing systems. The integration is designed around your current stack rather than a rebuild.

How do you reduce AI risk?

We use scoped use cases, permission boundaries, human approval, evaluation sets, monitoring, fallback behavior, and clear documentation. The Sinch rollback data above is the argument: failures trace to missing guardrails and diagnostics, so we build those first.

How is this different from your AI automation service?

AI automation connects the tools you already use into workflows, follow-up, routing, reporting. ML/AI development builds the models and systems themselves: RAG pipelines, predictive models, NLP workflows, custom LLM applications. Automation projects sometimes grow into ML projects once a workflow needs judgment at scale.

Do you fine-tune models or use RAG?

Retrieval first, almost always. RAG keeps knowledge current without retraining and costs a fraction of a fine-tune. We fine-tune when an evaluation set proves retrieval and prompt engineering have plateaued, typically narrow classification, strict output formats, or tight latency budgets.

Explore an AI use case

If you have an AI idea, we can help you decide whether it is worth building and what the first version should be, and we will say so plainly if it is not. Explore an AI use case.

Related services: software development when the model needs a product around it, backend development for the data and API layer underneath, and AI automation for workflow-level systems that do not need custom models.