Enterprise UX research for AI products

Turn your AI investment into a product people understand, trust, and choose to use

Find the trust and usability issues that could slow adoption before you put your AI product in front of users.

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What decides whether a person actually uses your AI product, or gives up on it.

Trust

Do people believe the AI’s output enough to act on it?

Understanding

Can users tell how it works, and when it’s wrong?

Control

Can they correct or override it when it slips?

Reliability

Does it hold up across real users and sessions?

Predictability

Do users know what it will do the next time?

Adoption

Will they keep using it, or drop it after one bad answer?

Gartner projects that over 40% of agentic AI projects will be cancelled by 2027, mostly because people don’t trust or understand them.

Trusted by Fortune 500 enterprises worldwide

Google-01 Tesla-01-1 Ebay-01-1 Adobe-01 1 Amazon-01 Cisco-01-1 Intel-01-1 Paloalto-01 BMO-01 AutoDesk-01 Walmart-01 Walt Disney-01-01-01 Anaplan-01-1 Acruex-1 FSB-1 Visa-01-1 Airbnb-01 General Electric-01 PayPal-01 Netflix-01 Twitter-01-1 Pintrest-01-1 VMWare-01 Informatica-01 Chase-01 Farmer Insurance-01 Gilead-01 PG&E-01 NBC-01 Caesars-01 Intuitive Omnissa-01 Broadcom-01 Infoblox-01-1 City National Bank-01 Servicenow-01-1 Equinix-01 Trimble-01 Lucid-01 PGE-01-1 Montefiore-01 PFU-01-1 Kaiser-01 ThermoFisher-01 Convergent-01 Chewy-01 CDW-01 Covetrus-01 Platform Science-01

Why AI needs its own research

AI needs research that goes beyond whether people can use the interface

When your AI is only right 80% of the time, how do users handle the other 20%? Do users know when to trust the AI’s answer and when to double-check it? Can users tell when the AI has given them a bad answer? Are users over-relying on the AI and skipping their own review? Can users correct the AI when its answer is wrong? How much explanation does the AI need to give before users trust its decision? Did a single hallucination make users distrust the AI from then on? Does the AI behave differently enough between sessions to feel unpredictable? Will users notice when the AI is working outside what it can reliably do?

Why teams choose Akraya for AI research

Specialized expertise for the parts of AI research generalist teams are not built for

Researchers who understand where AI fails

Your study is led by AI Interaction Engineers who understand LLM limitations, model behavior, and hardware constraints. That helps us design research around AI-specific failure modes before they reach users.

Deeper evidence on trust, comprehension, and reliance

Human Factors and Quantitative Psychology specialists measure how users understand, trust, and rely on your AI. You get evidence your team can use in product, risk, and leadership decisions.

Research that tests the AI’s decisions, not just the interface

For AI and agentic products, we evaluate the outputs, recommendations, and actions the system produces. That shows whether users understand what the AI is doing, trust it appropriately, and know when to question it.

Methods built for variable AI behavior

We use scenario testing, simulations, longitudinal studies, and decision-path analysis to surface issues that may only appear across different prompts, contexts, or repeated use.

Recruiting that reflects the users your AI will actually reach

We recruit across languages, cultures, user types, and edge cases, helping you catch trust, comprehension, and behavior issues before they show up in market.

Delivery that fits your release cycle

A PMO manages timelines, SLAs, and delivery from start to finish. You get specialized AI research without adding another project for your internal team to coordinate.

What teams come to us for

The AI product decisions teams
bring us in to de-risk

Before launching a new AI feature

We test how real users interpret the feature, where they get confused, and how they act on the AI’s outputs. You get prioritized findings while there is still time to fix the issues before launch.

When users are losing trust

We identify which outputs people doubt, ignore, or override and why. That helps your team see what is actually damaging confidence and what needs to change.

Before claiming the AI is explainable

We test whether users can understand how the AI reached a result, what influenced it, and when they should trust or question it. That gives you evidence that explainability works outside the team that built the product.

Before expanding to new users or markets

We test across different languages, cultures, user types, and edge cases to find failures that may not appear with your core participant group.

Before giving an AI agent more autonomy

For agentic products, we evaluate the decisions and actions the system takes on a user’s behalf. That helps you catch incorrect, risky, or confusing behavior before it reaches production.

When adoption is lower than expected

We study how people use the AI in real workflows, where they hesitate, and what stops them from relying on it. You learn what needs to change for the feature to become part of how they work.

Engagement models

Choose the AI research model that fits how your team works

Akraya delivers UX research as a managed service. That means we take responsibility for the research getting done, and give you two ways to bring that research in, depending on what your team needs.

Embedded AI research

Akraya manages the researcher and the program behind them, so you get dedicated research support without adding another person for your team to hire, supervise, and manage.

Best forOngoing foundational, evaluative, and roadmap research across an AI product.

Rapid AI research

Bring us one clear research question and we handle the rest. We scope the study, recruit the right participants, run the research, and deliver findings in 1–3 weeks.

Best forUsability testing, pre-launch validation, and time-sensitive AI product decisions.

Use both models when you need to

One product team may need an embedded researcher while another needs a rapid study before a launch. Both models run under one Akraya relationship, so you can match the model to the work without managing a different vendor for each need.

See how we work →

What you get

See what could stop users from trusting and adopting your AI before launch

Every study ends with clear, prioritized findings tied to the decision in front of you. You see where users lose trust, misunderstand the AI, or struggle to rely on it, plus what your team should change while there is still time to act.

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Security, IP & compliance

Built for confidential, compliant research inside AI environments

Keep sensitive model behavior inside your environment

When required, research can run inside your systems, so early model behavior, prompts, recordings, and outputs stay where your team controls them.

Test unreleased AI without exposing your company

Akraya can run studies as an independent third party and keep your brand hidden from participants. That lets you test pre-launch AI features and models without signaling what you are building.

Keep consent, data handling, and compliance covered

AI and privacy requirements vary by market and continue to change. We manage research consent, data handling, and compliance documentation across geographies so your team has a clear process behind the study.

Give procurement a clear engagement to approve

PMO-backed delivery, defined SLAs, and outcome-based scope make the engagement easier to evaluate as professional services. Procurement can see what is being delivered, how the work is managed, and what the spend covers.

From our clients

“

Their responsiveness, attention to detail, and collaborative approach have made them a valued extension of our team.

eBay
“

Their team has demonstrated exceptional responsiveness and a genuine caommitment to understanding CDW’s unique requirements.

CDW PMO Team

Comparing your options

When AI expertise matters, the research partner you choose matters too

What you’re weighing Build an internal team Generalist UX vendor Akraya AI Research
Best for Ongoing research you staff and own General usability and design feedback AI-specific research tied to a launch or decision
Understanding of AI failure modes Depends who you hire Tests AI like a normal app Researchers who understand how models break
Recruiting reach Limited to your own panels Standard participant pools Global, diverse, and edge-case recruiting
Compliance & model confidentiality Your team builds and owns it Varies by vendor Handled end to end, inside your environment
Time to start Months to staff Ramp plus onboarding Days to scope and begin

Scroll the table sideways to compare →

Akraya fits when you need AI-specific research now, want it managed end to end, and can’t spend months building the expertise, recruiting reach, and research infrastructure yourself.

Find out what could stop users from trusting your AI

Start an AI Research Request →

FAQ

Questions buyers ask before they commit

Can you test agentic AI products, not just chat interfaces? +

Yes. For agents, we test the decisions and actions the AI takes on a user’s behalf, using scenario and simulation testing and decision-path analysis. You learn whether users can rely on what it does, not just how the screen looks.

How do you protect our model, prompts, and unreleased features? +

Research can run inside your systems and as an independent third party. Sensitive model behavior, prompts, recordings, and findings stay in your environment, and we can run studies without revealing your company to participants.

Do you test the model itself or the user experience? +

Both meet in the work. We evaluate how real users understand and act on what the model produces, and we can support model evaluation and measurement frameworks alongside usability and trust research.

How do you recruit specialized or global participants? +

Recruiting is part of the engagement, including diverse, multilingual, and edge-case users your AI will reach. That’s how failures tied to culture, language, or unusual inputs show up before launch instead of after.

How do you measure something like trust? +

Our Human Factors and Quantitative Psychology specialists use validated behavioral measures, so trust, comprehension, and reliance are captured with evidence you can defend, not just quotes.

How fast can you start, and how long does a study take? +

We can scope and begin in days. Timelines depend on the design and how hard participants are to recruit, and we can run AI studies on a rapid timeline when a decision is close.

How much of our team’s time does this take? +

We run the research as a managed service, so execution sits with us, not your team. You bring the question and the access your researcher needs, and we handle scoping, recruiting, fieldwork, analysis, and the readout. Akraya also manages and supervises the researchers, so bringing us in doesn’t become another program for your team to run.

How does pricing work? +

We price research per engagement, scoped to the work delivered rather than by headcount, so what you pay maps to the research and the decisions it supports. Cost depends on the model and the scope: a single rapid study is scoped on its own, while embedded and research-as-a-service engagements are ongoing and sized to how much research your teams need. You can start with one study and expand from there.

How do you handle compliance as AI regulation changes? +

We manage consent, data handling, and compliance documentation across regions, and adjust as privacy and AI rules shift, so that risk sits with us instead of your team.

What if our AI product isn’t launched yet? +

That’s often the best time. Testing pre-launch means you find where the AI loses trust or breaks while you can still change it, and we can run confidential studies so nothing leaks before you’re ready.