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Dyyota vs Deloitte

Deloitte runs one of the largest AI practices in the world with deep expertise in regulated industries and existing audit relationships. Dyyota is a focused AI team that ships production systems in weeks at a fraction of the cost. Here is when each is the right fit.

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Dyyota vs Deloitte

Side-by-Side Comparison

Category
Dyyota
Deloitte
Team Size
3-8 specialists per engagement
15-50+ consultants across workstreams
Deployment Speed
3-6 weeks to production
4-12 months with extensive compliance and governance phases
Typical Cost
$50K-$200K per project
$500K-$3M+ per project
Specialization
AI agents, RAG, agentic automation
Full enterprise IT, strong in audit, tax, and regulated sectors
Post-Launch Support
Ongoing optimization and monitoring included
Managed services available as separate engagement
Engagement Model
Fixed-scope sprints with direct engineer access
Large cross-functional teams, formal governance structure

How the Two Actually Differ

Engagement model

Dyyota works in fixed-scope, fixed-price sprints of 2 to 6 weeks. We write a 3-page scope doc, agree on acceptance criteria, and ship working software every Friday. Most projects price between $50K and $200K, invoiced against sprint milestones. You get a shared Slack channel and direct access to the engineers writing the code.

Deloitte engagements run on time and materials against a detailed SOW with formal change request templates. Governance happens through a monthly steering committee, usually with 15 to 25 attendees, and a separate weekly program management call. Teams range from 15 to 50+ people split across a lead partner, a senior manager, multiple workstream managers, senior consultants, and a USI (Deloitte's India delivery arm) pod that does much of the hands-on work. Typical AI engagements price between $500K and $3M, with $750K to $1.5M being the common entry point.

Both models work. Dyyota optimizes for speed and cost-per-outcome. Deloitte optimizes for coverage, formal governance, and procurement defensibility in regulated environments.

Who actually does the work

At Deloitte, the staffing pyramid is explicit. A lead partner (Principal or Managing Director) owns the relationship and sells the work. A senior manager oversees delivery. Managers coordinate individual workstreams. Senior consultants and consultants run the daily cadence. The majority of the code and the ML work happens at Deloitte USI in Hyderabad and Bengaluru, where associate and senior associate engineers build under onshore architectural direction. The partner you met during the pitch is typically in the monthly steerco and little else.

Dyyota staffs 3 to 8 people on a project. Every person is a staff+ engineer who has personally shipped production AI systems. The architect writes code. The person who scopes your project writes the first pull request. There is no offshore handoff and no consultant-to-engineer translation layer.

This difference in staffing depth versus staffing ratios is the core structural split between the two models, and it shows up in both velocity and engineering judgment under ambiguity.

Speed to production

Dyyota ships a scoped AI agent, RAG system, or workflow automation in 3 to 6 weeks from kickoff to production traffic. Week 1 is scoping and architecture. Weeks 2 to 4 are build and integration. Weeks 5 to 6 are evaluation, hardening, and cutover.

Deloitte timelines for equivalent scope run 4 to 12 months. The standard cadence is a 6 to 8 week discovery and current-state assessment, a 10 to 14 week POC with a formal readout, a 4 to 6 month pilot in a sandboxed environment, and a production rollout handled by a managed services SOW. In regulated industries, add another 2 to 4 months for model risk documentation, IRM review, and legal sign-off. The gates exist because Deloitte's clients often require them. They also move the timeline from weeks to quarters.

If speed is your constraint and your regulatory surface is moderate, the math rarely favors the larger firm on a focused AI use case.

Risk profile

Every engagement model has a failure mode. Deloitte engagements fail through scope creep, cost escalation, and a mismatch between the depth of slides and the depth of the engineering. A $1.5M project becomes $4M over 24 months as change orders stack up and new stakeholders join steering. The governance artifacts are polished. The production code sometimes isn't, especially on AI-specific infra like retrieval quality, eval suites, and model drift monitoring.

Dyyota engagements fail through narrowness. We're not the right pick if you need 30 consultants running workstreams across change management, data governance, process redesign, and training. We're not the firm that will bring a formal model risk management package to a national bank regulator on day one.

Honest framing: Dyyota carries execution risk on scope boundaries. Deloitte carries cost and timeline risk on delivery and engineering depth. Pick the risk profile that matches your project and your regulators.

Cost breakdown

Here's what a $500K budget actually buys from each firm.

From Dyyota, $500K funds roughly 18 to 24 weeks of engineering across a 4 to 6 person pod. The breakdown lands near 70% engineering labor, 15% project management and scoping, and 15% overhead and tooling. You end up with 2 to 3 production AI systems shipped, documented, and supported, plus 6 months of post-launch optimization.

From Deloitte, $500K is typically below the floor for a full AI engagement. It usually funds a discovery and POC phase only. The breakdown lands near 35% engineering (mostly USI), 25% onshore project management and workstream coordination, and 40% partner time, senior manager time, methodology overhead, and margin. At the end of $500K, you usually have a current-state assessment, a target-state architecture, and a working POC in a sandbox. Production is a separate SOW.

The numbers aren't a judgment. They reflect what each model is built to do. Just don't confuse a $500K Deloitte assessment with a $500K production deployment.

Why Teams Choose Dyyota

  • You want production AI running in weeks, not a 6-month requirements gathering phase that ends in a 120-page current-state assessment.
  • Your project is a focused AI use case with a clear business owner, not a company-wide systems overhaul that needs 5 workstream leads.
  • You want to spend $150K on engineering instead of $750K on process, governance artifacts, and slide production.
  • You prefer working directly with the engineers writing your code over routing every question through a senior manager and a workstream lead.
  • You need a team that has shipped AI agents and RAG systems dozens of times, not one staffed from a 50,000-person bench with variable AI depth.

When Deloitte Is the Better Fit

  • You operate in banking, insurance, or healthcare payer operations and need a firm with established SOX, SOC 1, and model risk management infrastructure your regulator already accepts.
  • You have an existing Deloitte audit, tax, or advisory relationship and your CFO wants engagement continuity and a single relationship partner.
  • Your project requires integrating 10+ enterprise systems (SAP, Oracle, Workday, ServiceNow, Salesforce) and you need a large team just to manage the interface dependencies.
  • You need formal model validation documentation that goes to a board risk committee or a federal regulator before the system can go live.
  • Your internal compliance team requires a firm with Deloitte's established position on responsible AI and a published Trustworthy AI framework.

Frequently Asked Questions

Can Dyyota work in regulated industries?+
Yes. We've shipped AI systems for HIPAA, SOC 2, PCI, and GLBA environments, including healthcare claims work, payer operations, and regional bank use cases. We follow standard secure development practices, run data in the client's cloud account when required, and deliver architecture decision records and threat models as part of the build. Where Deloitte has a real edge is formal model risk management documentation for federally regulated institutions, deep audit-committee experience, and established relationships with the OCC, Fed, and state insurance regulators. If your project needs that level of regulatory interface, Deloitte is often worth the premium.
Is Deloitte better for large-scale integrations?+
If your project involves connecting 10 or more enterprise systems with complex data lineage, master data management, and formal data governance, Deloitte's team size and methodology were built for that shape of work. They can staff 30 people across data engineering, integration, MDM, and governance in week one. Dyyota is better when the scope is focused: 1 to 3 source systems, a clear business owner, and an outcome that can be measured in weeks. We're honest about the boundary. If your project is a full data platform replatform with an AI layer on top, call Deloitte.
How does pricing compare for similar AI projects?+
For a focused AI agent, RAG system, or workflow automation, Dyyota typically costs $80K to $200K end-to-end. The same scope at Deloitte usually runs $700K to $1.5M once you sum the discovery, POC, pilot, and production phases, with governance costs layered across each. The delta isn't about hourly rates; Deloitte's onshore bill rates are close to ours. The delta is team size, methodology overhead, and the partner-to-engineer ratio. At scale, Deloitte can also be cheaper per person-week at the USI level. At outcome level for a focused project, Dyyota typically costs 15 to 25% of an equivalent Deloitte engagement.
Can we use Dyyota alongside an existing Deloitte contract?+
Often yes, and this is common. Deloitte owns the broader program and Dyyota ships a specific AI workstream inside it. We've worked as a subcontractor under a Deloitte master agreement and in parallel where the client holds both contracts directly. The thing that has to be clear is interfaces: which system owns which data, which team fixes which bug, and who writes the runbook. When the program manager defines those boundaries, the model works. When they don't, you get scope collisions and we'd rather pass on the work.
What compliance frameworks does Dyyota support (SOC 2, HIPAA, GDPR)?+
Dyyota carries SOC 2 Type II certification. We ship under BAA for HIPAA-covered workloads, including routing through HIPAA-eligible AWS and Azure services and HIPAA-covered Anthropic and OpenAI endpoints. We support GDPR and CCPA data handling, including DSAR workflows, data residency in EU regions, and data retention controls. We deliver a completed HECVAT or client-specific security questionnaire as part of onboarding. What we don't do is ship our own ISO 27001 or FedRAMP authorization; if your project requires either of those, you'll need a larger partner or a specific subcontracting structure.
What happens if our scope is bigger than a single Dyyota engagement?+
We break it into a sequence of 3 to 6 week sprints with explicit milestones and a rolling roadmap. A typical large client runs with us for 9 to 18 months across 4 to 8 sprints. Each sprint has its own scope, fixed price, and acceptance criteria. You're not locked into a multi-sprint commitment; you can stop after any sprint and keep everything shipped. If you'd rather sign a larger frame agreement with a pre-agreed pipeline and volume discount, we offer that too. The default is sprint-by-sprint because it keeps incentives honest on both sides.
Does Dyyota produce the governance artifacts an auditor expects?+
Yes, proportional to the project. Every engagement produces an architecture decision record, a data flow diagram, a security threat model, an eval report with before-and-after metrics, and a runbook. For regulated projects we add a model card, a prompt-and-output log retention design, and a drift-monitoring plan. What we don't produce out of the box is the 200-page model risk management document a national bank's MRM function expects; for that level of artifact, you either hire Deloitte or bring in a specialist MRM consultancy to wrap our delivery. We're clear about that boundary up front.
How does Dyyota price a project before writing any code?+
We run a free 30-minute scoping call, then a paid $5K to $10K scoping sprint for any non-trivial project. The scoping sprint produces a written architecture doc, a data flow diagram, a risk register, acceptance metrics, and a fixed price for the build. Roughly 80% of scoping sprints convert to a build engagement. The other 20% either get shelved by the client or get referred to a different firm when we realize the shape of the work isn't what we're best at. We'd rather walk away from 20% of scopes than pretend we're the right fit for every project.

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