At Ema, we’ve never believed that AI should just assist. We believe it should own—own workflows, own accountability, own the job end-to-end.

That’s why we didn’t build “a better chatbot” or new co-pilot.

At Ema, we build Universal AI Employees—which are intelligent, coordinated, adaptive systems of AI agents that can tackle any business workflow end-to-end, whether in customer support, sales and marketing, employee experience—or anything else you can imagine.

These agentic systems keep learning from their current and past environments, get better with time, and collaborate with human teams—so they can focus on work that truly matters.

No more repetitive and rote work for humans. 10x the productivity for companies.

But this kind of intelligence isn’t enabled by the current state of LLMs in enterprises.

That’s why we built EmaFusion™, our proprietary model fusion technology:

• EmaFusion™ beats the best single models in the market—including GPT-4, Gemini, Claude, and O3—on both accuracy and cost.
• It fills a massive gap in enterprise AI by combining the best of 100+ models—because enterprises are overpaying for mediocre results, by using a single model for everything (or using basic model routing).
• EmaFusion™ offers a seamless plug-and-play experience, backed by enterprise-grade security and design—powering truly intelligent AI Employees and the future of work.

Why Enterprises Are Using LLMs Wrong

LLMs are rapidly commoditizing. The top models now cost less than $1 per million tokens, and new open-source options arrive every week.

But for enterprises, costs are still high and performance is inconsistent.

What gives? Most companies don’t even realize they are overpaying for subpar results by defaulting to one model—often GPT-4—for every task:

• Using expensive models for simple tasks: Summarizing a meeting note or classifying a support ticket shouldn’t cost the same as writing code or answering a nuanced legal question by reasoning across multiple systems and teams.
• Underperforming on complex or niche tasks: No single model works well for all tasks. GPT-4 might write well, Claude could reason better, Gemini might beat them both at multilingual tasks, open-source models can be great for domain-specific use-cases.

The best approach to model selection should be task-specific. Instead, after months spent on implementing a single model, enterprises are left with a large bill and infrastructure that is unsustainable and sub-optimal at scale.

"Each model has its strengths. Some are fast and cheap, others are powerful but expensive. Knowing which models to use when, and how to balance accuracy, costs and latency is a constant struggle for enterprises.” — Surojit Chatterjee, Founder & CEO, Ema Unlimited

But mapping tasks to the right model (or model-mix) is a hard problem to solve too.

Enterprises may manually route tasks to different models, or use simplistic model routing strategies, where an ML model or rule-engine attempts to classify prompts and pick a 'best-fit' model.

But even this basic model routing approach fails.

• Prompts aren’t atomic: Real enterprise tasks span multiple subtasks—like data extraction, validation, summarization, and recommendation—which no single model handles well in isolation.
• Accuracy, latency, and cost tradeoffs are unpredictable: The cheapest model might be too slow or less accurate, or the fastest model might hallucinate despite being expensive. These tradeoffs fluctuate across use-cases.

The best solution isn’t routing each task to any “best-guess” LLM—but about using the intelligence of all the models out there, now and in the future—with enterprise-grade security baked in.

This is how we make the most of the LLM revolution.

The Answer Isn’t Routing—it’s EmaFusion™

EmaFusion™ acts as a task-aware brain that does the thinking behind complex enterprise work. When an AI employee is asked to do something like review a contract, respond to an RFP, or generate a quarterly business review, EmaFusion™ first breaks the task into structured subtasks — things like extracting data, identifying risks, or generating language.

It then uses a self-optimizing system to decide which model is best suited for each step.

For instance, it might use a lightweight open-source model like Mistral for fast text extraction, a fine-tuned Ema model for structured data parsing, and escalate to costly models like GPT-4 only if the task requires deeper reasoning or creative drafting.

Once each piece is completed, EmaFusion™ fuses the outputs into a single, high-confidence result. Behind the scenes, it’s a coordinated system of model orchestration, cascading fallback logic, and task decomposition — but to the end user, it just feels like one intelligent AI that gets it right the first time.

We’ve spent months analyzing which models perform best on what tasks, and in what conditions and to what trade-offs, to build EmaFusion™:

1. Task-aware Routing: When we know the task type—say, summarization or classification—we use taxonomy to route requests to models we’ve rigorously evaluated for that category. This ensures every job starts with the most optimal and informed model choice possible.
2. Learned Router for Novel Tasks: When the system’s unsure or dealing with novel inputs, a learned router kicks in. This ML-powered component predicts the best model based on task embeddings, historical performance, and real-time feedback. It enables adaptability and precision when facing new, ambiguous tasks.
3. Confidence-Based Cascading Strategy: Why pay for GPT-4 when a smaller model gets the job done? EmaFusion™ always tries cheaper models first—and only escalates when confidence thresholds aren’t met. This keeps latency low and cost efficiency high without compromising on quality.
4. Task Decomposition and Fusion: However complex the problem, EmaFusion™ breaks it down into subtasks, routes each to the best model, and intelligently fuses the answers.

The result? EmaFusion™ achieves higher accuracy than any single model, including the top ones like GPT-4, Gemini, Claude, at a fraction of the costs.

EmaFusion™ in Action: Saving $$ and weeks, while giving the best answers

Say you’re working with a Fortune-500 client, who drops a 120-page Data-Processing Agreement on Friday at 4 p.m. They want to know:

“Which sections of the 2024 Africa Data-Protection Act govern sending biometric data to Singapore, and can you flag any non-compliant language in our draft?”

Here’s how EmaFusion would solve this (instead of the days spent waiting for multiple teams with tribal knowledge to figure this out):

• Instant triage: EmaFusion™ notices the query is legal, privacy-focused, and risk-sensitive.
• Starting small: A lightweight model skims the contract, pulling every statute citation and building a quick outline. That answers half the request for pennies.
• Targeted escalation: Unclear clauses (e.g., cross-border exceptions) trigger a jump to our in-house privacy model. It cross-checks Article 31, Section 12 against the client’s wording and flags two mismatches.
• Premium burst, only where it counts: For one particularly tricky clause, EmaFusion™ pings a top-tier commercial model plus a rule-based legal verifier. They rewrite the sentence and attach the exact statutory language.
• One fused answer: The system merges notes, highlights risky text in red, and produces a clean compliance checklist—ready to ship back to the client.

The Outcome?

• Get answers in 4 minutes end-to-end, not four billable hours.
• Cost stays in the “small-model” range because the expensive giant only handled 3 sentences.
• Legal team walks on Monday morning with a polished, statute-linked memo—no weekend scramble required.

That’s EmaFusion™ in action: start cheap, escalate smart, fuse everything into a single, confident answer your team can trust.

The Results: Better Than the Best

EmaFusion™ was tested on scores of such real enterprise workloads, across finance, legal, healthcare, R&D, customer support, and it outperformed single-model baselines in terms of accuracy, costs, and latency.

• 94.3% average task accuracy — higher than GPT-4o and Claude Sonnet, which score ~91.7%
• +17 points accuracy over GPT-4; +6 points over O1 and +9 points over top open source players like Deepseek-R1
• 20x cheaper than GPT-4. 4x cheaper than the average cost ($5.21 versus $16.29 per 1,000 prompts)
• Lower latency than top-tier models due to intelligent model selection and caching

EmaFusion™’s advantage isn’t just cost savings—it’s getting more reliable, high-quality outcomes across diverse enterprise workflows.

Seamless to Use. Secure by Design.

Get future-proof AI: EmaFusion™ combines outputs from over 100 models—private, public, domain-specific, open-source, including the latest releases like DeepSeek—so your AI investments are always optimized and future-proof.

Enterprise-grade security built-in: Ema's data governance redacts sensitive information before passing it on to public LLMs. Enjoy compliance with leading standards like SOC 2 Type I & II, ISO 27001, ISO 42001, HIPAA, GDPR, NIST CSF—and get unbeatable security with top-tier encryption and customizable, private models.

You’re always in control: Our customers can always control which models should be in the consideration set... You can also BYOM (bring your own model) and easily add to the model garden of EmaFusion™, so you can achieve the same quality in enterprise specific contexts.

You’re always in control: Our customers can always control which models should be in EmaFusion™’s consideration set. You can also BYOM (bring your own model) and easily add to the model garden of EmaFusion™, so you can achieve the same quality in enterprise specific contexts.

Get started in hours, not months: You can use EmaFusion™ out-of-the-box—no custom fine-tuning or model chaining required. Just plug EmaFusion™ into your stack through our API, or use Ema’s built-for-humans UI. EmaFusion™ readily integrates with 200+ of the most common enterprise apps and systems, such as Salesforce, ServiceNow, SAP, Workday, Snowflake and more.

Creating the Future of Work

The future of enterprise work isn’t hundreds of tools or brittle integrations. It’s making the most of AI Agents—by deploying a system of AI Employees that deeply understand business workflows and collaborate with human teams.

But for these AI Employees to work well, they need a brain that doesn't compromise—on accuracy, speed,or costs—at scale. That’s EmaFusion™.

Instead of forcing enterprises to choose, EmaFusion™ offers a new path: a future-proof, self-optimizing LLM architecture that’s optimized for how businesses really work.

It’s one of the most strategic, technical, and rewarding bets we’ve made at Ema.

And it’s ready for you now.

Read our full research paper on arxiv now: EmaFusion™: A Self-Optimizing System for Seamless LLM Selection and Integration

Book a demo today.