What Is Boost.ai? Features, Pricing and Who It Fits (2026 Guide)

Boost.ai is a Norwegian conversational AI platform, founded in 2016, that builds chat and voice virtual agents for regulated industries such as banking, insurance and the public sector, and it publishes no price list: Capterra's listing shows a starting price of $50,000 a year (checked 24 September 2026). Its best-known deployments are at DNB, where the Aino agent automates 50 to 60% of incoming chat, and Nordea, which runs 12 AI agents across four Nordic markets.

We build AI agents for customer service ourselves, so this is a practitioner's take. Boost.ai is a strong platform for the buyers it was built for and a poor fit for others; the sections below help you work out which one you are.

What is Boost.ai?

Boost.ai's homepage, positioning it as a conversational-AI platform for enterprise support.

Boost.ai is an enterprise conversational AI platform for building virtual agents (chatbots and voicebots) that automate customer support across chat and voice channels. Founded in Norway in 2016, the company has deep roots in European banking. The customers named on its homepage include DNB, Nordea, Santander, Metro Bank, Nordnet and Vipps, plus insurers such as Tryg and Staysure, telcos such as Telenor and Vodafone, and Michigan State University Credit Union. That heritage shows up everywhere: EU data residency, GDPR-first audit logging, a pre-built financial-services intent library, and integrations with the contact-center (CCaaS) and backend systems large European enterprises actually run.

Boost.ai now describes itself as "the conversational AI platform regulated industries trust," and its homepage leans on review scores rather than analyst badges: 4.8 out of 5 on Capterra and G2, and 94% would-recommend on Gartner Peer Insights, by its own count.

The concrete proof points are in its flagship banking accounts (all figures below are as reported by Boost.ai and its customers in published case studies, not independently verified):

• DNB (Norway's largest bank) runs a virtual agent called Aino that, per Boost.ai's case study, automates 50 to 60% of all incoming chat traffic and about 20% of total customer-service volume across all channels. Aino launched as first-line support in October 2018, passed 50% chat automation within six months, and has since interacted with over a million customers. DNB also runs an internal advisory bot, Juno, that the case study credits with 80% accuracy across 5,000+ daily users, and it keeps 15 full-time AI trainers maintaining the agents.
• Nordea operates 12 AI agents across four Nordic markets (Sweden, Denmark, Norway and Finland), with the case study reporting 90%+ in-scope resolution rates: 91% for private banking in Sweden and 95% for corporate customers. Its private-banking agents handle more than 220,000 conversations a month across the Nordics.

The short version: Boost.ai is a serious enterprise platform, tuned hardest for regulated industries where "the AI cannot go off-script" is a hard requirement rather than a nice-to-have.

How does Boost.ai's AI work?

The defining characteristic of Boost.ai is its hybrid model. Rather than betting entirely on a large language model, it orchestrates two layers and lets you decide, per use case, how much autonomy the AI gets:

• Traditional NLU (natural-language understanding): fine-tuned, multilingual intent models that give predictable, controllable, testable behavior for known, high-volume use cases (balance checks, card blocks, password resets).
• Large language models: layered on top for generative fluency, summarization, and handling the long tail of questions the intent models were never explicitly trained on, via prompt engineering plus Retrieval-Augmented Generation (RAG) over your own knowledge base.

Boost.ai frames this as giving enterprises "autonomy where it's safe, and control where it's critical." The LLM brings coverage and natural phrasing; the NLU layer and a stack of guardrails keep responses on-brand, accurate, and compliant. For a bank, that combination is the whole pitch: you get the conversational quality of generative AI without surrendering control over what the assistant is allowed to say or do.

Two newer capabilities are worth calling out because they define how Boost.ai has moved into the GenAI era:

• Generative Action: Boost.ai's approach to letting GenAI-powered agents actually do things (execute transactions, update records, complete multi-step workflows) rather than just answer questions, with guardrails suited to regulated, customer-facing environments.
• Test Studio: a persona-based automated testing suite that simulates real-world dialogues at scale to validate agent behavior before it ships. This matters more than it sounds: the hardest part of deploying AI in a bank is proving to risk and compliance that it behaves, and automated regression testing is how you keep proving it as the model and knowledge base change.

Around that core sit the enterprise controls: PII masking, role-based access, staging environments, audit trails, and approval/testing pipelines. Non-technical builders design and deploy through a visual, no-code UI.

What features does Boost.ai include?

• Hybrid AI orchestration: NLU + LLMs working together for both control and coverage, tunable per intent.
• Chat and voice, natively: Boost.ai positions voice as built-in rather than bolted on, so call-center automation and digital channels run on one platform and one set of conversation flows.
• Generative Action: GenAI agents that take real actions (lookups, transactions, record updates) with enterprise guardrails.
• Test Studio: persona-based automated testing to validate agents at scale before and after deployment.
• Multilingual, language-agnostic agents: fine-tuned language support that has notably extended to under-served European languages (including the Baltic languages) that most vendors skipped.
• Enterprise governance: PII masking, role-based access, audit trails, approval/testing pipelines, and guardrails.
• Industry solution libraries: pre-built intents and integrations for banking, insurance, and the public sector.
• No-code builder: a visual UI aimed at non-technical teams managing both chatbot and voicebot in one environment.
• CCaaS and backend integrations: pre-built connectors so agents can reach core systems and take end-to-end actions.

If your priority is having agents do things (look up an account, check a claim status, complete a transaction), the integration depth matters as much as the chat quality. (For a lighter, plain-English way to build those actions, compare with how custom tools work in a per-ticket model.)

How long does a Boost.ai deployment take?

Boost.ai's no-code builder means non-engineers can author and maintain conversation flows, which is one of its most-praised strengths in user reviews. But "no-code" does not mean "no project." A regulated deployment is still an enterprise implementation:

• Intent design and content. For a bank, the up-front work is mapping the real intents, wiring the pre-built financial-services library to your terminology, and connecting the RAG knowledge base to accurate, current source content.
• System integrations. The high-value use cases (checking a balance, blocking a card, filing a claim) require connecting the agent to core banking, CRM, and CCaaS systems, the part that turns a chatbot into something that resolves rather than deflects.
• Testing and sign-off. This is where Test Studio earns its keep. Risk, legal, and compliance typically need evidence the agent stays in-scope, which means simulated-dialogue testing, audit trails, and staged rollout rather than a flip-the-switch launch.
• Ongoing tuning. Reported resolution rates like Nordea's 90%+ are the result of continuous iteration on intents and content, not a day-one number.

Realistically, expect a sales-led evaluation, a scoped implementation (often with Boost.ai or a partner's solution team), and a timeline measured in weeks to a few months for a meaningful production agent, faster for narrow use cases, longer for broad, multi-system, multi-language ones.

Which channels, integrations and languages does Boost.ai support?

• Channels: web chat, in-app messaging, and voice (IVR/call-center automation), with the same agent logic reused across channels.
• Backend and CCaaS: pre-built integrations let agents execute transactions, update records, and complete workflows in enterprise systems, and connect into contact-center platforms so automated and human-handled conversations coexist.
• Languages: multilingual, language-agnostic agents (Nordea alone runs across four markets), with a track record of supporting less-common European languages other vendors ignored.

Is Boost.ai secure and compliant enough for a bank?

This is Boost.ai's home turf and the reason Nordic banks chose it. The platform is designed EU-first:

• EU data residency and GDPR-first audit logging so customer data can stay inside the EU, often a hard legal requirement for European financial institutions.
• PII masking to keep sensitive data out of logs and, where configured, out of the LLM path.
• Role-based access control, staging environments, approval pipelines, and audit trails, the governance surface that lets a risk team actually approve an AI deployment.
• Guardrails and Test Studio to constrain and continuously verify agent behavior in customer-facing, regulated contexts.

If your legal team's first question about any AI vendor is "where does the data live and can we prove what the bot said," Boost.ai has a genuinely strong answer. Always confirm the specifics (hosting region, sub-processors, and any on-prem or private-cloud options) for your exact regulatory context, since these are negotiated per deal.

How much does Boost.ai cost?

Boost.ai's pricing page.

Boost.ai does not publish list pricing or a self-serve calculator; this is a sales-led, quote-based enterprise product, and effective rates are negotiated per deal. That is the single most important thing to know before you evaluate it. The only public figure we found is on Capterra's listing, which shows a starting price of $50,000 a year (labelled per user, per year) as of 24 September 2026. Treat that as a rough floor rather than a quote; your number depends on the variables below.

What actually drives the cost:

• Volume: number of conversations/resolutions across your agents, which is the primary lever.
• Channels: voice automation is generally costlier to run than chat, so a voice-heavy deployment costs more than a chat-only one.
• Breadth and integrations: how many use cases, systems, and languages you deploy, and how much implementation help you need.
• Contract term and tier: higher-volume, longer commitments bring the effective per-conversation rate down.

Some third-party roundups describe outcome-based, per-resolution rates (on the order of ~$0.95 per resolved chat and ~$1.50 per resolved voice call at entry tiers). We could not confirm these as official Boost.ai list prices; they appear in analyst/aggregator content, and similar per-resolution figures are also quoted for other vendors, so treat them as directional, not authoritative. If a per-resolution model is on the table, get the exact definition of a "resolution" in writing before signing.

A few honest caveats:

• "Per resolution" pricing sounds clean, but resolutions can be defined generously; always confirm exactly what counts.
• At scale, voice automation can get expensive; model your actual volumes and channel mix before committing.
• Like most enterprise conversational-AI vendors, there is no true self-serve free trial; expect a sales-led proof of concept.

Whatever the per-unit rate, it is an enterprise purchase with a procurement cycle, not a swipe-your-card tool.

What are Boost.ai's pros and cons?

Pros

• Deep, credible expertise in banking, insurance, and public-sector compliance, with named, referenceable customers (DNB, Nordea, Santander).
• Hybrid NLU + LLM model gives real, tunable control over what the AI says and does.
• Strong enterprise governance: PII masking, audit trails, staging, approvals, and automated testing via Test Studio.
• EU data residency and GDPR-first design: a major plus for European regulated buyers.
• Genuinely no-code builder; reviewers consistently praise ease of use and the ability to manage chatbot and voicebot in one place.
• Native voice, not a bolt-on: one platform for digital and call-center automation.
• Broad, sometimes rare, language coverage.

Cons

• Enterprise-oriented; likely overkill and over-budget for small or mid-market teams.
• Opaque, quote-only pricing and no self-serve trial: you cannot evaluate it quickly or cheaply.
• Meaningful implementation effort and timeline for broad, multi-system deployments.
• Reviewers cite reporting and analytics as a relative weak spot: filtering, exporting, and customization could be better.
• Heaviest value is in regulated industries: general support teams may pay for compliance depth they will never use.
• If you live inside a single help desk such as Zendesk, Freshdesk or Gorgias, a full CCaaS-style platform is more machinery than the job needs.

Who is Boost.ai best for?

Boost.ai is an excellent fit for regulated enterprises (especially banks, insurers, and public-sector organizations, and especially in Europe) that need conversational AI across chat and voice with strict governance, EU data residency, and compliance controls. If your risk and legal teams need to sign off on every AI response, your data must stay inside the EU, and you are automating high-stakes financial workflows at scale, Boost.ai was practically built for you.

It is a weaker fit for smaller teams, non-regulated businesses, or teams that simply want to automate the repetitive tickets inside a single help desk, without standing up an enterprise CCaaS deployment, a procurement cycle, and a multi-week implementation.

On independent review sites the sample is small but consistent: Capterra lists Boost.ai at 4.8 out of 5 from 23 reviews, which we re-read on 24 September 2026, with the no-code builder and the support team drawing most of the praise. Boost.ai's homepage claims 4.8 on G2 as well; G2 blocked our requests, so we have not confirmed that score ourselves.

How does Boost.ai compare with Cognigy, Kore.ai and Macha?

Boost.ai competes at the enterprise/CCaaS end of the market with platforms like Cognigy and Kore.ai. That is a different segment from help-desk-native AI tools, which is why the honest comparison depends less on "who is better" and more on "what shape of problem you have." The table below frames the choice by use-case fit.

If Boost.ai looks like more platform than your team needs, the honest alternative is a lighter model. Instead of deploying a standalone conversational-AI platform, Macha runs as an AI agent layer on top of the help desk you already use: Zendesk, Freshdesk, Gorgias, Front, HubSpot or Intercom. It's done for you: the Macha team analyzes your past tickets, builds the knowledge base and the agent, and runs it in safe mode until the drafts are right. You pay per ticket on one published plan priced by volume ($299 a month for up to 750 tickets, $599 up to 1,500, $1,199 up to 3,000, about $0.40 a ticket), and that price includes setup and a dedicated success manager who handles your changes, rather than a negotiated enterprise contract. It is a genuinely different shape of product: where Boost.ai is a broad, governance-first, chat-and-voice platform tuned for regulated banking, Macha is a fast, transparent way to automate the tickets in the tool your team already lives in. See Macha pricing for the per-ticket model.

To be clear about the trade-off: for a Nordic bank that needs EU data residency, native voice automation, and a compliance-first hybrid model, Boost.ai's depth is hard to match, and Macha is not trying to. For a support team that just wants to resolve repetitive tickets inside its existing help desk without an enterprise procurement cycle, that same depth is cost and complexity you do not need. Pick by fit, not by hype.

How did we research this?

The product, deployment and compliance detail here comes from Boost.ai’s own site and documentation and from its published customer stories; Boost.ai does not publish prices, so the figures in the pricing section are the third-party and sales-reported ranges we could find at the time of writing, not a quote. On 24 September 2026 we re-read the Boost.ai homepage, the DNB and Nordea case studies and the Capterra listing, corrected the DNB all-channel figure from 22% to 20%, and replaced an unsourced "$5,000+ per month" directory estimate with Capterra's listed starting price. We did not obtain a quote. We have not deployed Boost.ai ourselves; there is no self-serve trial.

FAQ

What is Boost.ai? Boost.ai is a Norwegian enterprise conversational AI platform for building virtual agents that automate chat and voice support, with particular strength in banking, insurance, and the public sector. Founded in 2016, it is known for a hybrid NLU + LLM architecture and strong governance.

How much does Boost.ai cost? Boost.ai does not publish list pricing; it is a sales-led, quote-based enterprise product. Capterra's listing shows a starting price of $50,000 a year, but the real number is negotiated and driven by conversation volume, channel mix (voice costs more than chat), number of use cases and integrations, languages, and contract term. There is no self-serve free trial.

Is Boost.ai good for banks? Yes, banking is its core strength. Boost.ai offers EU data residency, GDPR-first audit logging, a pre-built financial-services intent library, and marquee customers including DNB, Nordea, Storebrand, and Santander. Per Boost.ai's own case studies, DNB's agent automates 50 to 60% of chat traffic and Nordea reports 90%+ in-scope resolution.

What is Boost.ai's hybrid AI? It combines fine-tuned NLU intent models with large language models (via prompt engineering and RAG), so agents get the fluency of generative AI while keeping the control, predictability, and testability of intent-based automation. You tune how much autonomy the LLM gets per use case.

Does Boost.ai support voice? Yes. Boost.ai treats voice as native rather than an add-on, so the same agent logic drives both digital channels and call-center/IVR automation from one platform.

What are Boost.ai's main weaknesses? Reviewers most often cite reporting and analytics as an area for improvement (filtering, exporting, customization). More broadly, it is enterprise-priced and enterprise-scoped, has opaque quote-only pricing, no self-serve trial, and a real implementation effort, so it can be overkill for small or mid-market teams.

How long does a Boost.ai deployment take? It varies with scope. Narrow, single-use-case agents can go live quickly; broad, multi-system, multi-language deployments with full compliance sign-off run weeks to a few months, including intent design, integrations, testing (via Test Studio), and staged rollout.

What are good alternatives to Boost.ai? For enterprise voice and contact-center AI, Cognigy and Kore.ai are the common comparisons. For teams that want to automate inside an existing help desk with transparent per-ticket pricing and $50 of free usage, Macha is a lighter-weight alternative that layers onto Zendesk, Freshdesk, Gorgias, Front, HubSpot or Intercom.

Want to automate support without an enterprise contract? Macha runs on top of Zendesk, Freshdesk, Gorgias, Front, HubSpot or Intercom: build agents in plain English and pay per ticket. Start a free trial at getmacha.com or see Macha pricing.

Sources: boost.ai, Boost.ai conversational AI platform, Nordea case study, DNB case study, Boost.ai empowers DNB to automate half its chat traffic (FF News), Test Studio (PR Newswire), Generative Action (PR Newswire), Capterra Boost.ai listing, G2 Boost.ai reviews, GetApp Boost.ai.