Frequently Asked Questions
Straight answers to the questions we hear most about enterprise AI consulting, AI agents, RAG systems, and working with Dyyota.
About AI Consulting
What is an AI consulting firm?
An AI consulting firm helps companies design, build, and deploy AI systems. This includes identifying the right use cases, choosing the right architecture, building production-ready solutions, and training internal teams. A good AI consulting firm brings both technical depth and business context so you get results, not just prototypes.
Do I need AI consulting or can I build in-house?
It depends on your team and timeline. If you have experienced ML engineers and 6+ months to spare, building in-house can work. Most companies hire a consulting partner for the first 2-3 projects to move faster, avoid common mistakes, and train their internal team along the way. A hybrid approach often delivers the best results.
Learn more about build vs. hireHow do I choose an AI consulting partner?
Look for production experience, not just demos. Ask how many systems they have running in production today, what their post-launch support looks like, and whether they can show real metrics from past projects. Avoid firms that lead with buzzwords and cannot explain their architecture decisions in plain language.
How much does AI consulting cost?
Enterprise AI consulting engagements typically range from $25,000 to $250,000 depending on scope. A focused AI agent project might cost $30,000-$60,000. A full multi-agent system with custom RAG and integrations can run $100,000-$250,000. We scope every project individually so you pay for what you need.
See our full pricing breakdownHow long does a typical AI consulting engagement take?
Most projects run 4 to 12 weeks from kickoff to production. A single AI agent or RAG system takes 3-6 weeks. More complex engagements with multiple integrations, custom training data, or compliance requirements take 8-12 weeks. We always start with a 1-week scoping phase to define deliverables and timeline.
What's the difference between AI consulting and hiring AI engineers?
Hiring AI engineers gives you long-term capacity but takes 3-6 months to recruit and ramp. AI consulting gives you a production-ready team on day one. Consulting works best for your first few AI projects or when speed matters. Once you have 3-4 systems running, building an internal team makes more sense.
Compare the two approachesAbout AI Agents
What is an AI agent?
An AI agent is a software system that can perceive its environment, make decisions, and take actions to achieve a goal without step-by-step human instructions. Unlike a simple chatbot, an agent can plan multi-step tasks, use external tools, and self-correct when something goes wrong. Enterprise agents handle tasks like research, data processing, and workflow automation.
Explore our AI agent solutionsHow are AI agents different from chatbots?
Chatbots respond to messages. Agents take action. A chatbot answers questions from a script or knowledge base. An AI agent can plan a sequence of steps, call APIs, query databases, send emails, and verify its own outputs. Agents operate autonomously across multiple systems, while chatbots are limited to conversation.
How much does it cost to build an AI agent?
A production-grade AI agent typically costs $30,000-$80,000 to build. The price depends on the number of tool integrations, the complexity of the decision logic, and whether you need features like persistent memory or human-in-the-loop approvals. Running costs (LLM API calls, infrastructure) add $500-$3,000/month.
See detailed cost breakdownHow long does AI agent deployment take?
Most enterprise AI agents go from scoping to production in 3-6 weeks. Simple agents with one or two tool integrations can ship in 2-3 weeks. Complex agents with multiple data sources, custom workflows, and compliance requirements take 6-8 weeks. We deploy iteratively so you see working software within the first week.
See our deployment processCan AI agents integrate with our existing systems?
Yes. Our agents integrate with any system that has an API or programmatic interface. This includes CRMs (Salesforce, HubSpot), ERPs (SAP, Oracle), databases, communication tools (Slack, email), cloud storage, and internal APIs. We build custom connectors where standard integrations are not available.
See integration capabilitiesHow do you ensure AI agent reliability in production?
Every agent we build uses a Planner-Executor-Reviewer architecture. The Reviewer layer validates all outputs before they reach your users or systems. We add observability pipelines, automatic retry logic, human-in-the-loop checkpoints for high-stakes decisions, and comprehensive logging so you can trace every action the agent takes.
About RAG Systems
What is RAG (Retrieval Augmented Generation)?
RAG is a technique that connects large language models to your private data. Instead of relying only on its training data, a RAG system retrieves relevant documents from your knowledge base and uses them to generate accurate, grounded answers. This means the AI can answer questions about your specific products, policies, and processes.
Explore our RAG solutionsHow accurate are enterprise RAG systems?
Well-built RAG systems achieve 90-95% accuracy on factual questions about your data. Accuracy depends on document quality, chunking strategy, embedding model choice, and retrieval configuration. We benchmark every RAG system against a test set of questions before launch and continuously monitor accuracy in production.
How long does it take to build a RAG system?
A production RAG system takes 3-6 weeks to build. The first week covers data ingestion, chunking strategy, and embedding pipeline setup. Weeks 2-4 focus on retrieval tuning, prompt engineering, and accuracy testing. The final phase handles deployment, monitoring, and user interface integration.
See our RAG development processWhat documents can RAG systems process?
Our multimodal RAG systems process PDFs, Word documents, spreadsheets, presentations, emails, web pages, images, and scanned documents with OCR. We also handle structured data from databases and APIs. The system automatically handles different formats and extracts text, tables, and visual information.
See multimodal RAG capabilitiesRAG vs fine-tuning: which should I choose?
Start with RAG in most cases. RAG is faster to build (weeks vs. months), easier to update (add new documents anytime), and does not require training data. Fine-tuning works better when you need the model to learn a specific style, format, or reasoning pattern. Many production systems combine both approaches.
Read our detailed comparisonHow much does enterprise RAG cost?
An enterprise RAG system costs $25,000-$75,000 to build, depending on data volume, number of document types, and accuracy requirements. Monthly running costs range from $300-$2,000 for embedding storage, LLM API calls, and infrastructure. Systems processing millions of documents sit at the higher end.
About Working with Dyyota
What industries does Dyyota serve?
We work across financial services, healthcare, legal, e-commerce, manufacturing, and professional services. Our AI solutions are industry-agnostic at the architecture level but customized for each sector's data, compliance, and workflow requirements. We have delivered production systems in 8+ industries.
How fast can Dyyota deploy an AI system?
Our fastest deployments go live in 2 weeks. A typical project takes 4-6 weeks. We move fast because we use proven architectures and pre-built components for common patterns like document processing, API integration, and monitoring. Every engagement starts with a 1-week scoping sprint to define exactly what we are building.
What does a typical Dyyota engagement look like?
Week 1 is a scoping sprint where we define the use case, architecture, and success metrics. Weeks 2-4 are build sprints with weekly demos of working software. Week 5-6 covers production deployment, testing, and team training. You get a dedicated Slack channel and direct access to the engineering team throughout.
Do you provide post-launch support?
Yes. Every project includes 30 days of post-launch support at no extra cost. This covers bug fixes, performance tuning, and monitoring. For ongoing support, we offer monthly retainer plans starting at $3,000/month that include monitoring, model updates, accuracy optimization, and priority support.
Can you work with our existing tech stack?
Yes. We build on your existing infrastructure rather than requiring you to adopt new platforms. We work with AWS, Azure, GCP, on-premise servers, and hybrid setups. Our solutions integrate with your current databases, APIs, authentication systems, and deployment pipelines.
See our integration approachHow do you handle data security and compliance?
We follow enterprise security standards by default. All data stays within your infrastructure or VPC. We support SOC 2, HIPAA, and GDPR compliance requirements. Our systems include audit logging, role-based access control, and data encryption at rest and in transit. We sign NDAs and DPAs before any data access.
About AI Strategy
Where should we start with enterprise AI?
Start with one high-value, low-risk use case. Look for processes that are manual, repetitive, and data-rich. Common first projects include document processing, internal knowledge search, or customer support automation. Avoid starting with your most complex or regulated workflow. Build confidence with a quick win first.
Take our AI readiness assessmentWhy do most AI projects fail?
The top three reasons are unclear problem definition, poor data quality, and no clear success metric. Most failures happen before any code is written. Teams build AI for the sake of AI instead of solving a specific business problem with a measurable outcome. Scoping is where projects succeed or fail.
Read the full analysisHow do I build a business case for AI?
Quantify the current cost of the process you want to automate. Include labor hours, error rates, and cycle time. Then estimate the improvement AI can deliver (typically 40-80% time reduction for well-scoped projects). Calculate the payback period by dividing project cost by monthly savings. Most AI projects pay for themselves in 3-6 months.
See our ROI frameworkShould I start with a pilot or go straight to production?
Start with a production-grade pilot, not a throwaway prototype. A 4-week pilot built on real data with real users gives you actual evidence to decide whether to scale. Throwaway prototypes prove AI can work in a demo but tell you nothing about production viability. Build it right from day one, just with a narrow scope.
How do I measure AI ROI?
Measure three things: time saved (hours per week), error reduction (percentage decrease), and throughput increase (volume handled). Set these baselines before you deploy. Track them weekly after launch. Most enterprise AI systems show clear ROI within 30-60 days of deployment when scoped correctly.
See our ROI measurement guideDo I need a Chief AI Officer?
Not yet for most companies. You need an AI-literate leader who can evaluate opportunities and manage vendors. That can be a CTO, VP of Engineering, or Head of Product. A dedicated Chief AI Officer makes sense once you have 5+ AI systems in production and AI is a core part of your business strategy.
Still have questions? Let's talk.
Book a free 30-minute call and we will answer anything specific to your business.