Production AI tools
that catch what demos miss.
Assess whether an AI system is ready to ship, estimate what it will cost at real volume, prioritize what to build next, and define exactly what an AI agent is allowed to do.
Ten free engineering tools built for teams moving LLM applications, RAG systems, voice agents, and AI agents from prototype to production. Use each tool without creating an account. Your inputs stay in your browser unless you choose to save, export, or request a technical review.
No account required · Browser-based calculations · Exportable engineering artifacts
From an AI prototype
to a production system.
A successful prototype proves that a model can complete a task under controlled conditions. It does not prove that the surrounding system is ready for real users, production traffic, sensitive data, or business-critical actions.
Before an AI feature ships, an engineering team still needs defensible answers to questions like these:
- How will quality be evaluated before every release?
- Can the system abstain, fall back, or escalate when confidence is low?
- Does retrieval respect the requesting user's permissions?
- What happens when chained model calls and retries multiply operating cost?
- Which agent actions require human approval?
- Can a model, prompt, or provider change be rolled back quickly?
- Can a harmful or incorrect output be traced after the fact?
- Who owns quality, cost, security, and incident response after launch?
Dwayo's engineering resources turn these questions into concrete scores, estimates, and policies that product, engineering, security, and finance teams can review together.
Free AI engineering tools
Three tools most teams reach for first, before anything gets built.
AI Use-Case ROI Prioritizer
Score and rank candidate AI projects by business value, feasibility, data readiness, and risk before committing engineering time.
Prioritize AI use cases → FeaturedProduction AI Readiness Kit
Run a 30-question production AI readiness assessment and get a prioritized remediation backlog across ten engineering dimensions.
Check production readiness → FeaturedLLM Cost & Capacity Calculator
Calculate production LLM cost per completed task using real request volume, retries, caching, and model routing.
Estimate cost per task →Decide what to build
Prioritize the use case, confirm it's production-ready, and estimate what it will cost — before engineering time is spent. Related services: AI Consulting, AI Readiness Assessment, LLM Integration.
AI Use-Case ROI Prioritizer
Compare AI opportunities before committing engineering time.
Score each candidate use case on business value, feasibility, data readiness, risk, and implementation effort, with adjustable weights that match your organization's priorities. See a quick-wins quadrant chart and a ranked, exportable backlog instead of whichever idea was pitched most recently.
Prioritize AI use cases by ROI →Production AI Readiness Kit
Check whether the system is ready to ship.
Complete a 30-question AI production readiness assessment covering evaluation, RAG quality, agent permissions, hallucination controls, cost management, observability, provider coupling, privacy, rollback, and operational ownership. Instead of reducing everything to one generic maturity score, the tool scores ten dimensions independently and produces a prioritized remediation backlog — so a strength in model quality can't hide a gap in access control or incident response.
Run the production AI readiness assessment →LLM Cost & Capacity Calculator
Estimate cost per completed task, not just per token.
Model monthly and annual inference cost using daily request volume, input and output tokens, model calls per workflow, retries, caching, model routing, and expected growth. The calculator shows cost per completed task, a 12-month projection, sensitivity at different traffic levels, and the relative impact of common optimization strategies — exposing the multipliers that simple token calculators miss, especially multi-step workflows, retries, and routing between model classes.
Calculate production LLM cost →Design it safely
Turn architecture and access decisions into reviewable documents before anything ships. Related services: AI Governance, AI Agent Development, LLM Integration.
AI Trust Spec Generator
Document AI privacy and safety controls.
Answer structured questions about data privacy, retention, model providers, permissions, guardrails, and auditability. Generate a Trust Spec document with open items flagged automatically — the single document security, legal, and enterprise customers actually ask for.
Generate an AI governance specification →Agent Permission Matrix Generator
Define agent approval boundaries.
Document every tool or action available to an AI agent and define its access scope, human-approval requirements, reversibility, retry limits, step limits, spend limits, confidence threshold, and escalation path. The generator converts those decisions into a YAML policy, CSV review matrix, and Trust Spec starter — one explicit document instead of assumptions scattered across prompts, source code, tickets, and chat threads.
Document AI agent permissions →Provider-Abstraction Starter Code
Avoid hard-coding one LLM provider.
A minimal interface and adapter pattern for LLM applications, in TypeScript and Python — one shared contract, adapters for OpenAI and Anthropic, and a factory that picks the active provider from config. Switching providers touches one file, not every call site.
Get the model-provider abstraction starter →Test it properly
Score retrieval, voice calls, and document extraction against real failure modes, not a demo script. Related services: RAG Development, Voice AI Development, Document AI, AI Agent Testing & Evaluation.
RAG Evaluation Starter Kit
Build a RAG test dataset, not a vibe check.
Score a golden set of test queries on retrieval relevance, faithfulness, citation accuracy, and answer correctness, then take home a starter Python eval harness that scores the same CSV schema in your own CI pipeline — a demonstration of the engineering depth behind real RAG evaluation.
Evaluate a production RAG system →Voice Agent Failure Test Pack
Evaluate escalation and out-of-scope calls.
A pre-loaded, editable set of voice-agent failure scenarios — interruptions, background noise, silence timeouts, accents, spoken PII, escalation, and dropped calls. Mark pass/fail against your own system and export a CSV test pack or markdown test plan.
Test a voice agent before launch →Document AI Benchmark Kit
Test OCR and extraction quality properly.
Estimate document-level extraction accuracy, straight-through processing rate, human-review volume, latency, and cost — using field count and confidence thresholds instead of a single headline accuracy number that compounds differently across every document.
Benchmark document extraction accuracy →Operate it in production
Prepare for the failure modes that only show up after real traffic arrives. Related services: MLOps, LLMOps & AgentOps.
Production AI Incident Runbook
Prepare for AI system failures before they happen.
Select the AI failure modes relevant to your system — hallucinations, cost spikes, provider outages, prompt injection, retrieval failures, agent loops, data leakage, and latency degradation — and generate a tailored runbook with detection signals, containment steps, rollback, and a postmortem checklist.
Build a production AI incident-response runbook →Which tool
should I use?
If you already know the decision in front of you, skip straight to the tool that answers it.
Built around real
production AI failure modes.
These tools focus on the failures that often appear after a promising demonstration meets real traffic and real systems.
- An agent enters a retry loop and creates an unexpected cost spike.
- A RAG system retrieves information the requesting user should not be able to access.
- A high-impact tool is exposed to an agent without an approval boundary.
- A provider change breaks application code that is tightly coupled to one SDK.
- A team cannot reproduce or trace a harmful model output.
- A new prompt or model is released to all users without a rollback path.
- Quality and cost degrade because nobody owns them after launch.
The tools do not certify that an AI system is safe, compliant, or production-ready. They make assumptions visible, provide a structured engineering baseline, and help teams decide what to investigate next.
Who these AI
engineering tools are for.
Built for the people who have to answer for a production AI system, not just demo it.
Before you run
the first tool.
What these tools are, and what they aren't.
Are Dwayo's AI engineering tools free?
Yes. All ten tools — covering planning, evaluation, governance, and incident response — can be used without creating an account. An email is requested only when you choose an optional action such as saving a result, exporting a detailed artifact, or requesting a Dwayo engineer's review.
Is information entered into the tools uploaded to Dwayo?
The tools are designed to calculate and generate results in the browser. Inputs are not sent to Dwayo unless you explicitly choose to save, export through a server-supported workflow, or request a review.
Do these tools certify that an AI system is safe or compliant?
No. They are engineering planning aids, not security audits, legal advice, compliance certifications, or guarantees of production safety. Their purpose is to identify assumptions, gaps, and decisions that require further validation.
Can these tools be used for RAG systems and AI agents?
Yes. The readiness assessment includes criteria for retrieval quality, citations, document permissions, agent tool access, human approvals, fallback behavior, observability, privacy, and rollback. The permission generator is specifically designed for tool-using AI agents.
Can a Dwayo engineer review the result?
Yes. After completing a tool, you can optionally share the result with a Dwayo engineer. The review focuses on the assumptions, risks, and next engineering decision revealed by the result — not a generic sales pitch.
Start with the six-minute
Production AI Readiness Kit.
See the complete result without creating an account. If you want a second opinion, share the scorecard with a Dwayo engineer after the assessment.