Helikai Brings Enterprise-Grade AI Agents to Business Workflows With Micro AI Approach

Helikai's micro AI agents deliver 99%+ accuracy for enterprise workflows with 200+ pre-built solutions across IT, legal, and healthcare at IT Press Tour.

At the 66th IT Press Tour in January 2026, Helikai presented a refreshingly pragmatic approach to enterprise AI that addresses why so many AI initiatives fail to deliver business value. Instead of pursuing ambitious, all-encompassing AI systems, the company builds purpose-specific "micro AI agents" that solve discrete business problems with 99%+ accuracy—then chains them together for complex workflows.

The Problem: AI Projects That Never Ship

Helikai co-founder Jamie Lerner opened with a story many executives will recognize. As CEO of a public company, he issued the standard AI mandate: everyone needs to use AI because it's the future. A year later, despite smart engineers and meaningful projects, they had little to show for it.

"I think that's fairly typical of most companies when they begin working in AI," Lerner said.

"So I turned around and said, just give me something; make something simple work. Just use AI to validate an address, calculate shipping, something simple."

That shift from "let's build ambitious AI" to "let's automate specific tasks" became the foundation of Helikai's methodology. The company now offers a catalog of 200+ pre-built AI agents (called Helibots) that handle everything from invoice processing to legal document analysis to medical image interpretation.

The Micro AI Difference

The distinction between Helikai's "micro AI" and traditional "macro AI" approaches comes down to scope and accuracy:

Macro AI (OpenAI, Google, Anthropic):

• General-purpose models trained on broad datasets

• Results vary by version and provider

• Usage-based pricing creates unpredictable costs

• Designed for large-scale enterprise deployments

• Good for employee productivity, creative work, general queries

Micro AI (Helikai):

• Purpose-built agents: one agent, one outcome

• Predictable cost, scope, and delivery timeline

• Agents chain together for complex workflows

• Runs on-premise or in private cloud

• Enterprise-grade accuracy (99%+) through focused training

The business case centers on reliability. "If you're running an ERP system, you can't have it calculate taxes a little differently every time," Lerner explained. "You can't have it send to a different shipping address a little differently every time. You need true enterprise-grade reliability."

Business Use Cases Across Industries

Helikai's agent catalog spans multiple domains:

IT & Business Operations:

• Automated invoice and purchase order processing

• Proposal and quotation generation with CPQ integration

• Semantic search across corporate knowledge bases

• IT service desk automation (password resets, access requests)

• Conversational AI for customer support and sales

Sales & Revenue Optimization:

• Pipeline health inspection (flagging incomplete opportunities)

• Cross-sell identification in existing customer base

• Whitespace analysis for prospecting

• Forecast accuracy improvement

• Renewal automation with customer health checks

Legal:

• Contract analysis and generation

• eDiscovery prioritization and classification

• Motion analysis and brief drafting

• Litigation intelligence (predicting outcomes based on past rulings)

• Deposition analysis with inconsistency detection

Healthcare & Life Sciences:

• Clinical trial eligibility matching

• Medical image analysis (radiology, pathology)

• Clinical documentation and EHR tagging

• Literature mining for research

• Consent and ethics compliance tracking

Media & Entertainment:

• Multilingual subtitle generation

• AI voiceover and dubbing

• Film restoration and quality assurance

• Metadata tagging and catalog indexing

• Visual effects generation

Insurance:

• Contract analysis for insurance requirements

• Coverage gap identification

• Claims processing automation

• Policy comparison and validation

The Workshop-First Methodology

Helikai doesn't lead with technology. They start with a business-focused AI workshop that uses the MITRE AI Maturity Model to assess organizational readiness:

Step 1: Identify automation opportunities. "Discuss and list potential projects, risks, impact on the business, and overall complexity. Get a sense of what use cases are natural fits for AI automation, and which to avoid."

Step 2: Rank by risk and maturity. "Stack rank the use cases to identify the 'low hanging fruit' to get started with. Ideally leveraging pre-built Helibots on a less complex, yet important use case."

Step 3: Select one high-ROI, low-risk focus. "While many projects may be appealing, it is critical to focus on the success of one use case."

Step 4: Document requirements. "Fully document the current workflow and desired automation outcomes, data inputs and outputs, integration points, stakeholders, timeline, budget, security requirements."

The output: an agile business requirements document and budget estimate for implementing specific agents from their catalog.

This approach addresses a critical insight: most enterprises overestimate their AI readiness. "What you'll find quickly is most companies are not that mature," said co-founder Ross Fujii. "So we question why they want to start with something so complex."

Real-World Example: Retail Furniture Photography

Lerner described working with one of the world's largest furniture retailers. The initial project focused on product photography—a massive cost center when you're photographing millions of SKUs with hundreds of fabric options.

"Nobody brings in a million pieces of furniture to photograph. So what they do is create a 3D rendering of their sofa, then artists put all the hundreds of different fabrics on it. No one shoots photographs—they're generated in Photoshop. Then it's put in a background, put in a living room that doesn't exist. Artists take hundreds of hours to do this for millions of pieces of furniture at millions of dollars of expense."

Helikai's agents automate the 3D rendering, fabric application, lighting calculation, and shadow generation. "We can calculate the sun coming in through windows, from every light. The AI does it easily." The result: scientifically accurate shadows and lighting that would take humans days to paint by hand.

But the project expanded. The CEO wanted to "talk to my data"—asking questions like "What are my best-selling couches?" or "Why don't these rugs sell in Europe?" Helikai built a semantic interface to the data warehouse, generating SQL queries from natural language questions and formatting results as tables, PDFs, or PowerPoint slides.

"We might start with purchase orders, but then you consumed all the data about my products, so you can do product Q&A. You consumed all my HR documents, so you can do employee Q&A. All private, not shared with anyone, completely access-controlled."

Data Security & Sovereignty

The top concern preventing enterprise AI adoption: data security. Helikai addresses this directly with their SPRAG (Secure Private Retrieval Augmented Generation) platform.

Key security features:

• Fully on-premise deployment with zero internet connectivity

• No data sharing between customers (even in cloud deployment)

• Complete audit logging of every AI decision

• Role-based access control integrated with enterprise systems

• Customer retains 100% data ownership

"Most customers want their RAG system to be entirely private, not in a multi-tenant model, completely private to them," Lerner said. "They believe they're building their own intellectual property off their data, and they do not want that shared with us or anyone else."

For regulated industries (financial services, healthcare, legal), this architecture means AI adoption doesn't require sending sensitive data to third-party providers.

Deployment Models & Pricing

Helikai offers three deployment approaches to match different business needs:

1. Helikai Enterprise (On-Premise)

• SPRAG servers located on-premise, in colo, or virtualized in public cloud

• Customer manages infrastructure (upgrades, patches)

• Standard support (9am-5pm M-F) or Premium support (24x7x365)

• Hardware ranges from $22K entry-level to $500K+ for large-scale deployments

2. Helikai SaaS Cloud

• Monthly subscription for Helibots and SPRAG

• Data isolation (no multi-tenant sharing)

• 12-month minimum commitment

• Zero physical infrastructure to manage

• Premium support included

3. Pay-as-you-Go

• Per-project, per-document, per-image pricing

• Individual pricing for each Helibot

• No infrastructure investment or ongoing commitment

The pricing philosophy: "Human minus 15%." For tasks currently done by humans, Helikai typically charges 85% of the human cost while delivering faster results with higher consistency.

Example: A customer had 10,000 contracts requiring 4-6 hours of human review each—two people working full-time for 600 days. "We said we'll do it in two days and 15% cheaper. When you get the speed and the consistency with all the auditability, it's a no-brainer."

Why Projects Succeed

Helikai's approach addresses the main reasons enterprise AI projects fail:

1. Focused scope: one agent, one outcome. No scope creep, predictable timelines.

2. Pre-Built Solutions: 200+ agents ready to deploy, not starting from scratch.

3. Matched to Maturity: Start simple, build complexity as capabilities mature.

4. Enterprise-Grade Accuracy: 99%+ accuracy through hybrid AI + deterministic approaches.

5. Quick Time to Value: Deploy pre-built agents in weeks, not months or years.

6. Data Sovereignty: Fully private, on-premise deployment option with complete control.

The Human-in-the-Loop Philosophy

A key differentiator: Helikai doesn't pursue fully autonomous AI. Their KaiFlow orchestration layer makes human intervention straightforward.

"We can look at everything occurring in these complex pipelines and anywhere instruments are human-in-the-loop," Lerner explained. "Ask a human, stop, get someone to form an opinion."

Example: An invoice processing agent might flag unusual discounts for manager approval rather than auto-processing potentially fraudulent transactions. A legal document agent might highlight contract clauses with ethical concerns for attorney review.

"Our agents do have personalities. They have voices; they have names. People actually build relationships because they're working with their agent all day long." The KaiFlow companion becomes a collaborative tool rather than a replacement.

The Competitive Landscape

How does Helikai compare to major AI platforms?

vs. Public AI Services (ChatGPT, Gemini):

• Public AI: Broad scope, continuously updated, pay-per-token, good for productivity

• Helikai: Focused scope, private data, unlimited usage, good for business automation

vs. Hyperscaler AI (AWS, Azure, Google Cloud):

• Hyperscalers: Platform lock-in, usage-based pricing, multi-tenant

• Helikai: Model-agnostic, flat pricing, fully isolated

vs. Custom Development:

• Custom: Long timeline, high cost, maintenance burden

• Helikai: Pre-built catalog, fast deployment, managed service

The sweet spot: enterprises needing production-grade AI for specific business processes without the overhead of building and maintaining custom solutions.

Looking Ahead: First Half 2026

Helikai's roadmap focuses on making agent development more accessible:

• AI agent development templates and UI

• Workflow/pipeline templates

• Integration with Power Automate, AI Builder, N8N

• New agents: DICOM image redaction, Snowflake/Salesforce interfaces, pediatric radiology, invoice fraud detection

The vision: enable business users to compose workflows from pre-built agents without writing code, while giving developers the flexibility to customize when needed.

The Bottom Line

Helikai's micro AI approach offers a pragmatic path to enterprise AI adoption:

✓ Start with proven, pre-built agents for specific business problems

✓ Deploy privately with complete data control

✓ Achieve enterprise-grade accuracy (99%+) through focused training

✓ Scale incrementally as capabilities mature

✓ Avoid vendor lock-in with model-agnostic architecture

For enterprises struggling to move AI projects from pilot to production, this focused approach may be precisely what's needed. Rather than trying to boil the ocean, Helikai automates specific waves—one reliable agent at a time.