AI Product Development
Pick the right AI model based on your data, not headlines
New models appear every month. We test the candidates on your real tasks and data, measure what matters to you, and recommend a model with evidence behind it.
The problem
The problem this solves
Choosing an AI model has become confusing. Every few weeks a new model claims to be the best. Public benchmarks measure general skills that may have little to do with your work. Prices, speed and data policies differ widely. Teams either pick the most famous model and overpay, or pick the cheapest and live with poor results.
The only reliable way to choose is to test on your own tasks. A model that tops a benchmark may be worse at extracting fields from your invoices, following your tone of voice or answering questions about your products than a cheaper one. A reasoning model may be excellent for complex analysis and needlessly slow and expensive for simple classification.
We build an evaluation set from your real examples, with the correct or preferred outputs agreed by your team. Then we run each candidate model through it and measure accuracy, consistency, made-up answers, speed and cost per task. We include open-weights models when data must stay on your own servers, and we check each provider's data terms.
The result is a clear recommendation, often a mix: a strong model for the hardest tasks, a fast, cheap one for the rest. You also keep the evaluation set, so you can re-run it when new models appear and switch with confidence rather than guesswork.
Inference costs often decide the business case, so we model them at your expected volume, not just per request. Our model comparison tool and directory show the models we usually consider.
What you get
What you get
-
An evaluation set
Real examples from your business with agreed correct or preferred outputs.
-
Success measures
Accuracy, consistency, tone, speed and cost criteria weighted for your use.
-
Side-by-side results
Every candidate model scored on the same tasks, with examples of where each fails.
-
Cost projection
Monthly cost estimates at your expected volume for each model.
-
Data policy review
Where data goes, how it is retained, and whether it is used for training.
-
Recommendation report
A plain-language recommendation with the evidence and trade-offs.
-
Reusable test suite
Scripts to re-run the evaluation when new models are released.
-
Routing plan
Which model to use for which task, if a mix gives the best value.
How we build it
How we build it
-
1
Define the tasks
We list the tasks AI will do and how a good result is judged.
-
2
Build the test set
Examples collected and correct outputs agreed with your team.
-
3
Run the models
Candidates tested with consistent prompts and settings.
-
4
Review results
Your team reviews scores and sample outputs, not just numbers.
-
5
Recommend
A written recommendation with costs, risks and a switching plan.
AI and people
Where AI helps, where people decide
AI makes the repetitive parts faster. The decisions that shape your product stay with experienced people.
Where AI speeds things up
-
Running thousands of test cases across models quickly.
-
Scoring outputs automatically where criteria are clear.
-
Grouping failures into patterns for review.
-
Estimating costs from token counts at your volume.
-
Drafting the results report.
Where people decide
-
What counts as a correct or good output.
-
How to weigh accuracy against cost and speed.
-
Which data policies are acceptable.
-
Whether automated scores match human judgment.
-
The final choice and when to re-evaluate.
Is this right for you?
When this is the right choice
A good fit when
-
You are about to commit to an AI model for a product or process.
-
AI costs are growing and you suspect a cheaper model would do.
-
You need evidence for a decision, such as for leadership or compliance.
Consider something else when
-
You are only experimenting and any capable model is fine for now.
-
You have no examples of the task yet. Collect some first.
Timeline and cost
What affects the timeline and cost
A focused evaluation of a few models on one or two tasks usually takes one to three weeks, mostly driven by how quickly examples and correct answers can be collected.
We do not publish fixed prices because scope drives cost. How we estimate.
-
Number of tasks
Each task needs its own examples and criteria.
-
Number of models
More candidates mean more runs and review time.
-
Example preparation
Creating correct answers is often the largest effort.
-
Human review
Subjective tasks, such as tone, need people to rate outputs.
-
Self-hosted models
Testing open models on your own hardware adds setup.
-
Compliance review
Regulated industries need deeper data policy checks.
Keep exploring
Related services, solutions and reading
Related services
View all related services- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- AI agents AI that takes actions in your systems, such as qualifying leads or processing requests, with people checking the results.
- User management and authentication Sign-up, login, roles and permissions done properly, including single sign-on for business customers.
- RAG knowledge assistants Assistants that answer questions from your own documents and show where each answer came from.
- Document automation Read invoices, forms, contracts and IDs, pull out the right fields and route them for review.
- AI chatbots Website and messaging chatbots that answer common questions well and hand everything else to a person.
Solutions
View all solutions- AI document processing Read forms, applications, IDs and statements, extract the fields you need and route each document for the right review.
- AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
- Compliance tracking Track filing deadlines, licenses and obligations in one place, with reminders and AI summaries of relevant rule changes.
- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- Contract review assistant Highlight unusual clauses, missing terms and deviations from your standard positions, so reviewers focus where it matters.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
Industries
View all industries- Finance and accounting Client portals, document collection, invoice and receipt processing, and reporting for accounting firms and finance teams.
- Healthcare Patient booking, intake forms, internal knowledge assistants and admin automation for clinics and care providers.
- SaaS and startups MVPs, subscription billing, AI features, multi-tenant platforms and scaling support for founders and product teams.
Case studies
View all case studiesGuides and articles
View all guides and articlesAI models
View all ai models- Reasoning Models that think through multi-step problems before answering: analysis, planning, math, complex documents and agent work.
- Open weights Models whose weights you can download and run on your own servers or a cloud of your choice.
- Claude Fable 5.1 The top tier of the Claude family, for the most demanding reasoning and long-horizon agent work, priced above Opus.
- Gemma 4 Google's open-weight model family under Apache 2.0, in sizes from phone-friendly to 31B, with image input and function calling.
- Llama 4 Scout An open-weight Llama 4 model with image input and a very long context window, for teams that want to host a capable model themselves.
- Llama 4 Maverick The larger Llama 4 open-weight model, with image input and a 1 million token context window, for self-hosted assistants and analysis.
Glossary terms
View all glossary terms- Hallucination A hallucination is when an AI model states something false or invented as if it were true, such as a made-up fact, figure or source.
- Inference Inference is the step where a trained AI model is used to produce an output, such as an answer, a label or a prediction, from new input.
- Reasoning model A reasoning model is an AI model that works through a problem step by step before answering, trading speed and cost for better results on hard tasks.
- Open-weights model An open-weights model is an AI model whose trained parameters are published, so anyone can download and run it on their own hardware under its license.
- Context window A context window is the maximum amount of text, measured in tokens, that an AI model can consider at once, including the question, documents and its answer.
- Fine-tuning Fine-tuning is further training an existing AI model on your own examples so it learns a specific style, format or task.
FAQ
Questions about AI model evaluation
Have a question that is not here? Ask us directly.
It may be the best choice, but often a cheaper or faster model does your specific task just as well. Testing on your data shows the real trade-off.
When a major new model is released, when prices change noticeably, or every few months. With a reusable test suite, re-running takes little effort.
Yes. Open-weights models such as Llama, Mistral, Qwen or Gemma can be tested and self-hosted when data must stay in-house. See our open-weights models.
Read our guide how to choose an AI model for your business.