Introducing ACR:
Adaptive Context Resolution

Your AI can answer anything. ACR teaches it to ask first.

Intelligence isn't just knowing answers—it's knowing when to resolve context by asking questions AND knowing what to ask.

AI today is ReAct-ive: input triggers immediate output, right or wrong. ACR makes your agents intelligently deliberate—systems that think before they respond and learn your ambiguity overtime.

Deliberative intelligence. Finally possible.

📉
30–40% Lower LLM Costs
✓
60%-90% Efficiency Gains
📈
30% Higher User Satisfaction
✓ Built on peer-reviewed CLARINET research ✓ SOC 2 Type II Compliant ✓ Model-agnostic
Confident Agent answers immediately ✓ Correct intent Wrong Wrong object updated ✗ Wasted tokens

The $500K Problem Hiding In Your AI Deployment

Enterprise LLMs waste 30–40% of their budget answering ambiguous questions they should clarify first. Ark AI makes your agents intelligently deliberate— resolving context before acting, turning unreliable systems into production-grade assistants.

Confident Errors

"Update the pricing for enterprise customers."

Your agent confidently updates the database pricing... when they meant the test script made 8 turns ago.

Cost: Emergency rollback + angry client calls

Budget Black Hole

Every ambiguous prompt triggers 5,000-50,000 wasted tokens.

At $500K/month LLM spend, $150K-200K is pure waste from misunderstood queries.

Reality: You're subsidizing hallucinations.

Trust Erosion

"The AI is smart but I never know if it understood me."

Users abandon AI features not because they're wrong 100% of the time—but because they're unpredictably wrong.

Impact: Low adoption despite high investment.

These aren't isolated incidents. They're symptoms of a fundamental architecture gap in LLM deployments.

How Adaptive Context Resolution (ACR) Works

1

Intelligent Detection

Every query analyzed for contextual ambiguity—not just surface-level confusion.

Example: "Schedule a meeting" is unambiguous at a 10-person startup, highly ambiguous at an enterprise with 6 meeting types.
2

Targeted Clarification

Generate the minimal, precise question needed to resolve uncertainty.

❌
Not: "Can you provide more details?"
✅
But: "Should I schedule this as an internal sync or a client presentation?"
Distinguishing questions that prevent $50K mistakes from ones that simply annoy users.
3

Reinforcement Learning

Our system learns your ambiguity thresholds over time.

•
Which questions users appreciate vs. tolerate
•
Which clarifications prevent errors vs. slows workflows
•
How different teams use the same terms differently

Result: Agents that get smarter every month, automatically.

All Major AI Labs Already Do It

The biggest AI labs already use resolution-first agents wherever the cost of being wrong is high and margins are thin.

⟨⟩ Claude Code

Clarification before code execution.

When tasks touch complex codebases or production-like workflows, Claude Code increasingly asks targeted follow-up questions instead of guessing. That behavior exists because errors are expensive—and internal margins matter.

🔍 ChatGPT - Deep Research

Ask more, assume less.

Deep research-style tools lean on multi-step reasoning and repeated clarification to reduce high-cost mistakes in analysis and decision-making. The more the work matters, the more the system slows down and asks.

🎬 Video Orchestration

Clarification before expensive generation.

When generating long, expensive outputs like high-fidelity video, systems interleave questions, previews, and refinements before committing resources. Clarifying upfront is cheaper than regenerating from scratch.

Ever wonder why you don't see that same behavior when you call their base models directly?

Because when you use raw APIs, you're the one subsidizing the waste. Their internal products supplement models with resolution layers to protect their own margins—your deployments rarely do. We help you stop paying for guesswork.

The Ark AI Impact

60%-90%
Higher Efficiency
Ambiguous requests are clarified before they trigger wrong actions.
30–40%
Cost Reduction
Average enterprise saves $150K–$200K annually on $500K LLM spend.
30%
Higher NPS/Satisfaction
Users rate agents as "professional" and "careful".
10-Minute
Integration
One API call. No model retraining required.

Calculate Your Savings

Estimated Monthly Waste
-
Ark AI Annual Savings Range
-
Payback Period
-

Why Senior AI Leaders Are Paying Attention

2024
Research Foundation

Peer-reviewed frameworks for clarification question generation emerge at ACL, EMNLP, SIGIR.

We didn't invent the science. We engineered it for production.
2025
Industry Reality

Studies show 72% of LLM user dissatisfaction remains unresolved even when users attempt clarification.

Current approaches don't scale. Context-adaptive systems do.
2026
Market Timing

As enterprises move from pilots to production agents, efficiency and reliability become non-negotiable.

Early adopters gain 12–18 months advantage in deployment maturity.
"The path from GPT wrapper to production-grade agent requires solving ambiguity resolution at scale. Most companies discover this after their second failed deployment."
— Research from Anthropic's "Building Effective Agents"

Stop Subsidizing Hallucinations

Your AI budget should drive results, not waste. Let us show you exactly where your agents are guessing wrong—and how much it's costing you.

Get in Touch

  • See Ark AI detect and resolve real ambiguities from your workflows.
  • 30-minute technical deep-dive
  • Custom ROI analysis for your deployment
  • No-pressure consultation

Try our Beta

Experience ACR risk-free. No credit card required.

✓ SOC 2 Type II Compliant ✓ Your data never leaves your infrastructure ✓ Model-agnostic (GPT-4, Claude, Gemini, custom)

Frequently Asked Questions

Why can't I just improve my prompts to handle ambiguity? +
You can reduce ambiguity by 10-20% through prompt engineering. We reduce it by 60%+ because we learn YOUR specific context over time. The difference: static instructions vs. adaptive intelligence.
Won't GPT-5 or Claude Opus 5 solve this? +
Smarter models reduce obvious errors, but they still don't know your end user's context. "Update the dashboard" means different things to different teams. Raw intelligence < contextual understanding.
How is this different from fine-tuning our model? +
While some use cases do overlap, fine-tuning costs you $50-100K+ and 3+ months per use case. When your product changes, you start over. Ark AI adapts in real-time across all workflows through continuous RL—no retraining required on your end.
What if OpenAI or Anthropic adds this feature? +
They profit from token consumption. We profit from efficiency. Incentive alignment matters. Plus, we're model-agnostic—work with any LLM, open-source or proprietary.
This feels like overkill for our deployment. +
Fair question. At 10,000 queries/month or $50K/year in LLM spend, you're right—handle it manually. But at 10,000 queries/day and $500K+/year spend, you're leaving your users frustrated at least 20% of the times and leaving $150K–$200K on the table annually. That's when efficiency infrastructure becomes essential.
How long does integration take? +
2-3 days for most deployments. One API call before your direct LLM call. We handle ambiguity detection and question generation; you keep your existing prompt/context engineering.