AI consulting services: senior AI engineers who advise, then build
Hyparz AI consulting is delivered by senior, pre-vetted AI engineers who help you decide what to build, prove it is feasible on your data, and then build it with the same people. It is designed for startups and small to mid-sized businesses that need a working AI feature or automation, not a slide deck. Engagements start with a short discovery sprint, typically matched within days, and run under a one-week risk-free trial with a dedicated account manager.
Hyparz is a remote-talent marketplace that matches companies with pre-vetted remote software and AI engineers, typically within days, with a one-week risk-free trial.
Why teams choose Hyparz for this
Advice from people who will ship it
Large consultancies hand you a strategy and leave the build to someone else. Our consultants are senior engineers who have shipped LLM apps, RAG systems, agents and ML models, so every recommendation is one they expect to implement themselves. That keeps the plan honest and the handover cost at zero.
Scoped for a startup budget
We start with a fixed, short discovery sprint rather than an open-ended retainer. You get a clear answer on feasibility, a recommended architecture and a prioritised roadmap, then decide whether to continue into a prototype with the same engineer, add a second specialist, or stop with the findings in hand.
Deeper than a solo consultant
An independent consultant is one person with one background. Hyparz draws on a vetted network across ML, LLM applications, RAG, AI agents, MLOps, data engineering and workflow automation, so when your discovery reveals a data problem or an integration problem, the right specialist can join within days.
Hyparz at a glance (October 2026)
Vetting pass rate (AI roles)
About 5% of applicants pass
About 5% of the AI engineers who apply to Hyparz pass vetting, which tests real-world ability, system design and communication on practical tasks.
Indicative hourly rates (USD)
- Mid-level AI engineer: $60 to $100 per hour
- Senior AI / ML engineer: $110 to $190 per hour
- MLOps / AI infrastructure specialist: $120 to $200 per hour
Indicative bands for engineers matched through Hyparz; they track the rates Arc.dev publishes for vetted freelance AI talent. Toptal does not publish rates. Your quote depends on specialism, seniority and engagement length. Sources: Arc, freelance AI developers for hire (October 2026): $60 to $100+ per hour; Arc Employer Blog, freelance developer rates 2026 (updated May 2026): AI/ML engineering $110 to $190, MLOps $120 to $200.
What you get
- Use-case discovery workshop with your product, ops and data owners
- AI readiness assessment: data access, quality, volume and labelling gaps
- Feasibility test on a sample of your real data, with measured results
- Build vs buy recommendation with named vendors, models and trade-offs
- Target architecture: models, retrieval, orchestration, hosting and guardrails
- Evaluation plan: success metrics, test sets and human review points
- Prioritised roadmap with phases, team shape and risks
- Optional working prototype in the same engagement
How it works
Discovery sprint
One to two weeks with a senior AI engineer: interviews, data review, candidate use cases scored on value, feasibility and risk, and a written recommendation you can act on immediately.
Feasibility and prototype
The highest-value use case is tested on your real data. You see measured accuracy, latency and cost before any larger commitment, and the prototype becomes the base for production.
Build and scale
The same engineer continues into production, or we add specialists (data engineering, MLOps, frontend) from the same network. Your dedicated account manager adjusts the team as the scope moves.
Where this delivers results
Fintech and financial services
Fraud and anomaly scoring, document and KYC extraction, reconciliation automation and customer support agents, all of which need audit trails, explainability and human review. See our dedicated page on AI development for fintech for the practices we apply in regulated environments.
Healthcare and life sciences
Clinical and administrative document processing, intake triage, coding assistance and research search over internal literature. Consulting here focuses on PII handling, human-in-the-loop review and keeping clinical decisions with clinicians, with the technical controls designed around the requirements your compliance team sets out.
Ecommerce and marketplaces
Product content generation at catalogue scale, search and recommendation, returns and support automation, and demand forecasting. Discovery usually finds that two or three workflows carry most of the value, and that one of them needs classic ML rather than a language model.
SaaS and internal operations
Adding an AI feature to an existing product, grounding an assistant in your documentation with RAG, or automating back-office workflows with n8n, Zapier, Make or Airflow. We help you pick the pattern that fits your data and your team, then staff it.
Do you need an AI consultant or an AI engineer?
You need consulting when the question is still open: which use case, which approach, whether your data can support it, and what it will cost to run. You need an engineer when the decision is made and the work is to build it. Most companies need both in sequence, and the cheapest path is to have the same senior person do the thinking and then the building.
A quick way to decide is to look at what you can write down today. If you can state the input, the output, the quality bar and the data you have, you are ready to hire an AI engineer. If any of those is a guess, start with a discovery sprint. There is a third case: a company with no technical leadership that needs someone to own the technology strategy, the hiring and the vendors over the long term. That is a CTO-level leadership role, not a consulting engagement, and we will say so in the first call rather than stretch a project engagement into a job it is not designed for.
- Consultant: the problem and the approach are not yet defined; you need options, evidence and a roadmap.
- Engineer: the approach is defined; you need it built, tested and shipped.
- CTO-level leadership: you need ongoing ownership of technology, people and vendors, beyond any single project.
What does an AI discovery sprint look like?
A discovery sprint is a fixed one to two week engagement with one senior AI engineer. It starts with interviews across the people who own the process you want to improve, then a review of the data that process produces. The engineer lists candidate use cases and scores each on business value, technical feasibility and risk, then tests the strongest one on a sample of your real data.
The output is written, not presented: a readiness assessment, a build vs buy recommendation, a target architecture and a phased roadmap. The habit of testing on real data early matters more than it sounds. Gartner (July 2024) predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs and unclear business value. Every one of those is something a discovery sprint is designed to surface before you spend on a build.
The questions we answer most often
The same handful of decisions come up in nearly every engagement, so we have written our general reasoning down. Build or buy? Buy when a vendor already sells your use case and it is not a differentiator; build when the value comes from your data, your workflow or an integration no vendor offers. Our build vs buy AI framework walks through the scorecard we use.
RAG or fine-tuning? Retrieval first for anything that depends on your documents and changes over time; fine-tuning for style, format and narrow tasks where you have labelled examples. The RAG vs fine-tuning guide covers the trade-offs. Which automation platform? n8n, Zapier or Make each fit a different team and pricing model, which the n8n vs Zapier vs Make comparison explains. Is our data ready? Usually partly. The sprint tells you exactly which gaps block which use cases, and which can be worked around.
What drives the cost of AI consulting?
We do not publish a single price because the inputs vary too much between companies, but the drivers are predictable. The number of candidate use cases and stakeholders sets the discovery length. The state of your data decides whether feasibility can be tested in days or needs a data engineer first. The degree of integration with existing systems, the compliance context, and whether you want a prototype inside the sprint all move the scope. Seniority of the engineer and whether a second specialist joins round it out.
The structure keeps the risk on our side at the start. The engagement begins with a one-week risk-free trial; if the engineer is not the right fit you do not pay for that week and you keep any work delivered. After discovery, you choose between continuing with an individual expert, a blended team of AI and product engineers, or a full remote team, with automated timesheets and source-code protection on every model.
+19284448604 
hello@hyparz.com 
Hyparz- 1260 N Arizona Ave 
