AI Consulting for CTOs
AIDOLS helps CTOs ship production AI in 90 days inside the reference architecture they already own — no vendor lock-in, no proprietary platform, no army of analysts. Every engagement is staffed with senior engineers who write code on the call, ships full MLOps from day one, and hands you the model weights and the source repository at the end of the SOW.
What CTOs get from AIDOLS
Specific outcomes scoped against the metrics the CTO office already tracks — not generic value props.
Production-grade architecture, not a notebook prototype
AIDOLS ships every Build with a real production stack: containerized inference services, evaluation harness, observability, drift detection, model versioning, and CI/CD. CTOs get a system their on-call engineer can operate, not a Jupyter notebook the consultancy disowns at the SOW close.
MLOps from day one, not bolted on later
Model serving, feature pipelines, evaluation harnesses, and monitoring are part of the Build deliverable — not a separate $200K follow-on engagement. AIDOLS standardizes on open infrastructure (Kubernetes, vLLM, MLflow, OpenTelemetry, dbt) so the system runs on your existing platform team.
No vendor lock-in. You own the weights and the repo.
AIDOLS hands over source code, model weights (where applicable), evaluation datasets, and infrastructure-as-code at the close of every Build. There is no proprietary platform you have to keep paying for and no lock-in to a specific cloud vendor or model provider — model routing is built in.
Engineers who write code on the call
Every AIDOLS engagement is staffed with senior engineers who are personally writing the code. There is no partner-leverage model where a $400/hr partner sells the work and a $80/hr offshore analyst delivers it. The engineer on your architecture review is the engineer on your production cutover.
The 4 problems we solve for CTOs
Consultancies ship notebooks. You inherit the productionization problem.
The dominant pattern in AI consulting is a 4-12 month engagement that produces a Jupyter notebook with promising offline metrics and a deck recommending the client "operationalize" the model. The CTO inherits the productionization problem: model serving, feature pipelines, drift detection, A/B infrastructure, evaluation harness, on-call runbook. The productionization work is typically 3-5x the cost of the original engagement and lands on the CTO's team without budget.
AIDOLS scopes every Build around a production cutover, not an offline accuracy metric. The deliverable is a deployed inference service with the surrounding MLOps stack, an evaluation harness wired to your CI, and an on-call runbook the platform team can adopt. The CTO does not inherit a productionization debt — the system ships ready to operate.
Vendor lock-in is the silent cost of most AI consulting.
A common pattern is a consultancy or platform vendor delivering AI capability inside a proprietary stack — bespoke serving infrastructure, opaque model registry, locked feature store, or a single-cloud-region hard dependency. The CTO discovers the lock-in when the contract renewal arrives, when the cloud bill spikes, or when the team needs to add a model the vendor does not support. Migration cost out is typically 6-18 months and a full rebuild.
AIDOLS standardizes on open infrastructure (Kubernetes, vLLM or TGI for inference, MLflow for model registry, OpenTelemetry for observability, dbt for transformations) and ships source code, weights, and infrastructure-as-code at SOW close. There is no proprietary AIDOLS platform to keep paying for. CTOs can run the system on AWS, GCP, Azure, or on-premise without rework.
You cannot afford 18 months of build to discover the use case does not work.
Most enterprise AI engagements run 12-18 months from kickoff to first production deployment because they sequence assessment, strategy, design, and build into separate phases handed between separate teams. By the time the system reaches production, the underlying business assumption has shifted, the sponsor has rotated, or the data layer has changed. The CTO carries the cost of the failed engagement on the engineering capacity ledger.
AIDOLS compresses the loop into a 90-day Build with a single team. Architecture, data engineering, model development, integration, and cutover happen in parallel under one engineering lead. The CTO gets a binary outcome inside a quarter: shipped + measured against a written KPI, or refunded.
AI hires take 6 months to land. You need capacity now, without a permanent headcount commitment.
A senior MLOps engineer or staff ML engineer takes 4-9 months to recruit at $250K-$350K fully loaded. Most CTOs need the capability before that timeline allows, and many cannot defend the headcount commitment until the first production system is shipped and proving ROI. The result is a chicken-and-egg problem where the AI program waits on hiring and hiring waits on the AI program.
AIDOLS' Scale retainer at $25K/month covers ongoing MLOps capacity without a permanent headcount commitment. Most CTOs use Scale for the first 6-12 months post-launch while they hire the in-house team, then transition operations in-house once one production system is proven and the headcount case is defensible.
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See exactly where production AI fits inside your operations — with a concrete 90-day roadmap scoped to a CTO mandate.
Start the Assessment →How a CTO engagement typically works
Four steps from kickoff to production cutover. Fixed fee at every step.
CTO + engineering kickoff
Architecture review with your CTO, principal engineer, and platform lead. AIDOLS engineers walk your existing stack, agree the integration surface area, and lock the SOW with a fixed fee and a 90-day production date.
Architecture + data layer
Production architecture designed against your existing stack. Data pipelines built with dbt or your existing transformation layer. MLOps surface (model registry, feature store, evaluation harness) wired to your CI/CD.
Build + integration
Model development, inference service build, and integration into the application layer in parallel. Weekly architecture review with your engineering team. PRs land in your repo, not a vendor sandbox.
Production cutover + handover
Canary deploy, ramp to production, evaluation harness verified against the agreed KPI. AIDOLS hands over source, weights, infrastructure-as-code, and on-call runbook. Optional Scale retainer for ongoing MLOps coverage.
Pricing model that fits a CTO's budget cycle
Three transparent fixed-fee tiers — no time-and-materials drift, no partner-leverage uplift, no surprise change orders.
Best for: CTOs scoping the architecture and MLOps stack for a first AI use case before greenlighting a Build. Produces a defensible architecture decision record.
- •Data + infrastructure audit
- •Prioritized opportunity map with ROI estimates per use case
- •Target-state reference architecture
- •90-day implementation roadmap with named owners
- •Executive readout deck
Best for: CTOs ready to ship one production AI system in 90 days inside their existing reference architecture. Includes full MLOps and source handover.
- •Production AI system shipped end-to-end
- •Data engineering, model development, MLOps deployment
- •Integration into existing systems
- •Post-launch evaluation harness + monitoring
- •100% ROI guarantee against a written KPI
Best for: CTOs who need MLOps capacity without a $250K-$350K headcount commitment, typically while hiring a permanent team.
- •Ongoing engineering retainer post-deployment
- •Model retraining, monitoring, drift detection
- •Incident response + evaluation harness updates
- •One new feature release per month
- •Replaces a $250K-$350K fully-loaded MLOps hire
AIDOLS vs Big Four for a CTO
| Dimension | AIDOLS | Big Four |
|---|---|---|
| Pricing model | Fixed fee per tier; written, no T&M drift | Time-and-materials retainer; partner-leverage multiplier on engineer rates |
| Engineering depth | Senior engineers who write code on the call | Partner-led sale; offshore analysts deliver against the SOW |
| Deliverable | Production AI system + source + weights + IaC handover | Notebook + strategy deck; productionization scoped separately |
| Time to production | 90 days end-to-end | 6-18 months across phased SOWs |
| Architecture posture | Open stack (Kubernetes, vLLM, MLflow, OpenTelemetry, dbt) | Often proprietary platform with vendor lock-in |
| MLOps coverage | Included in Build; full evaluation harness + drift + on-call runbook | Separate $200K-$500K productionization SOW |
| Vendor lock-in | Zero. Source + weights + IaC transfer at SOW close | Proprietary serving stack; migration is a 6-18 month rebuild |
| Exit clarity | 60-day cancellation on Scale; clean handover to in-house team | Annual prepayment + termination fees; tribal knowledge stays with the firm |
What a CTO should ask before hiring an AI consulting firm
Seven questions to put on every shortlist call. Firms that cannot answer crisply on all seven are not engineering-first.
- Which engineer is personally writing the code, and do they stay on after the SOW is signed?
- What is the production architecture, and does it run inside our existing reference stack?
- What MLOps surface is included — model registry, feature pipelines, evaluation harness, drift detection, on-call runbook?
- Do we own the source code, the model weights, and the infrastructure-as-code at SOW close?
- What proprietary components, if any, will we be locked into post-deployment?
- How many production AI systems has the firm shipped in the last 12 months, and can I review the architecture decision records?
- What is the on-call runbook, and who carries the pager between SOW close and Scale-tier handover?
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Run the ROI Calculator →Common objections from CTOs — and our honest answers
"My team can build this in-house. We do not need a consultancy."
Most can — but not in 90 days, and not while also keeping the existing platform on. The right comparison is 90-day Build at $75K-$150K vs. 9-month in-house build with a hiring overhang. AIDOLS frequently bridges the gap: ship the first system, hand it to the in-house team, transition operations.
"We do not want another consultancy with proprietary platform lock-in."
Zero proprietary AIDOLS platform components in any Build. The stack is Kubernetes, vLLM/TGI, MLflow, OpenTelemetry, dbt — open and portable. Source, weights, IaC transfer at SOW close. You can run the system on any cloud or on-premise.
"We tried this with a Big Four firm and got a notebook."
That is structural to the partner-leverage model. AIDOLS scopes every Build against a production cutover, not an offline metric. The fixed fee covers the full production stack — model serving, MLOps, evaluation harness, drift detection — not a notebook plus a productionization upsell.
"We have a security review process that takes 8 weeks."
Most AIDOLS engagements run security review in parallel with Weeks 1-4 architecture work. We deploy inside your VPC and your existing IAM/secrets management. SOC 2 Type II controls are documented up front. The 90-day clock starts after security approval if your process requires it.
"What happens if your engineer leaves mid-SOW?"
Every Build is staffed with a primary engineer and a backup engineer; both are on the architecture and code reviews from Week 1. The on-call runbook is written progressively, not at the end. If a primary rolls off, the backup is already up to speed — no knowledge cliff.
Three anonymized CTO engagement patterns
Generic descriptors used to protect client confidentiality. Detailed reference architectures and named references available under NDA.
Series B SaaS CTO
Series B SaaS CTO engaged AIDOLS to build an in-product AI copilot inside their existing Kubernetes platform. KPI: copilot adoption among paid users + per-session inference cost ceiling.
Manufacturing CTO at a $50M-revenue plant
CTO at a single-plant industrial manufacturer engaged AIDOLS to deploy predictive maintenance on the highest-revenue CNC line, integrating with existing MES and SCADA via OPC UA.
Mid-market financial services CTO
CTO at a mid-market lender engaged AIDOLS to rebuild fraud detection inside their existing AWS environment without a model registry vendor commitment.
FAQs from CTOs
How does AIDOLS staff an engagement for CTOs?
AIDOLS staffs every Build with a primary senior engineer and a backup engineer, both on the architecture review from Week 1. There is no partner-leverage model where a partner sells the work and an offshore analyst delivers it. The engineer who writes the code is the engineer on every architecture review and the production cutover.
What does the AIDOLS production architecture look like?
AIDOLS standardizes on open infrastructure: Kubernetes for orchestration, vLLM or TGI for inference serving, MLflow for model registry, OpenTelemetry for observability, dbt for data transformations. Every Build deploys inside your existing VPC, your existing IAM, and your existing CI/CD. There are zero proprietary AIDOLS platform components.
Do we own the source code and the model weights?
Yes. AIDOLS hands over source code, model weights (where applicable), evaluation datasets, infrastructure-as-code, and the on-call runbook at the close of every Build. There is no proprietary platform you have to keep paying for and no migration risk if you choose to operate the system entirely in-house.
How does AIDOLS handle MLOps?
MLOps is part of the Build deliverable, not a separate engagement. Every Build ships with model serving, feature pipelines, an evaluation harness wired to your CI, drift detection, version control on training data, and an on-call runbook. The Scale tier ($25K/month) provides ongoing coverage if you do not yet have an in-house MLOps engineer.
How does AIDOLS handle security review and procurement gates?
AIDOLS runs security review in parallel with Weeks 1-4 architecture work. We deploy inside your VPC and your existing IAM and secrets management. SOC 2 Type II controls and data-handling agreements are documented up front. The 90-day clock starts after security approval where your process requires it.
Can AIDOLS build inside our existing reference architecture?
Yes — that is the default. AIDOLS does not impose a reference architecture or a target-state platform. We deploy inside your existing Kubernetes (or alternative orchestration), your existing data platform, and your existing CI/CD. Architecture decision records are produced for your engineering team to keep.
How does AIDOLS price compared to hiring an MLOps engineer?
A senior MLOps engineer runs $250K-$350K fully loaded and takes 4-9 months to recruit. The AIDOLS Scale tier covers equivalent ongoing MLOps capacity at $25K/month with 60-day cancellation notice. Most CTOs use Scale for the first 6-12 months post-launch while hiring the permanent team, then transition operations in-house.
What happens after the 90-day Build closes?
You have three options: (1) operate the system entirely in-house using the source, weights, IaC, and runbook AIDOLS hands over; (2) move into a Scale retainer at $25K/month for ongoing MLOps coverage; or (3) extend into a second Build for the next use case. There is no automatic renewal and no multi-year commitment.
AI consulting for other roles
AIDOLS works across the full executive stack. If your engagement spans multiple functions, we scope it jointly.
Three ways to start, scoped to a CTO mandate
Pick the path that matches the next decision your office has to make.