Pricing informed by a decade of shipped AI projects
A cross-section of what we ship — the problem, the approach, and the number that moved. Placeholder examples below; real client stories replace these before launch.
Underwriters lost hours to manual policy lookups. A RAG assistant now answers from their own document set in seconds, with citations.
Trucks ran half-empty on guesswork. A forecasting model now predicts demand by lane and rebalances routes a week ahead.
Shift handoffs meant re-reading long charts. An LLM summariser drafts a structured handoff note the clinician approves in seconds.
Keyword search missed intent. Vector search now understands “warm waterproof jacket under $100” and ranks the right results.
Agents copy-pasted from scattered docs. A copilot drafts grounded replies from the knowledge base, so agents edit instead of write.
Manual QA missed hairline defects at line speed. An edge vision model flags them in real time before parts move downstream.
The build stage sets the order of magnitude; the type of system sets where you land inside it. These are typical end-to-end ranges — discovery through a deployed, integrated first version.
| Project type | Typical range | Timeline | What drives the cost |
|---|---|---|---|
| AI chatbot / assistantRAG over your docs & data | $15k–70k | 4–10 wks | Content volume, accuracy bar, integrations |
| LLM agent / workflow automationMulti-step, tool-using | $30k–120k | 8–16 wks | Number of tools, guardrails, human-in-loop |
| Custom ML modelPrediction, forecasting, scoring | $20k–150k | 10–20 wks | Data readiness, accuracy target, retraining |
| Computer vision systemDetection, inspection, OCR | $50k–180k | 12–24 wks | Labelling, edge vs cloud, latency |
| Recommendation / personalizationRanking & relevance | $40k–140k | 10–20 wks | Data pipelines, real-time scoring |
| Generative AI in-product featureCopilot, drafting, summarization | $25k–100k | 6–14 wks | Model choice, evals, safety, cost-per-call |
| Data & ML platformPipelines, feature store, MLOps | $60k–250k | 12–28 wks | Data sources, governance, scale |
Ranges assume a production-grade build, not a throwaway demo. A scoped pilot to de-risk the biggest unknown almost always costs less than the low end above.
Two companies asking for “an AI assistant” can get quotes 4× apart — honestly. Here’s what accounts for the gap, ordered by how much it moves the number.
The single biggest swing. Clean, labelled, accessible data can halve a build; messy or scattered data is where most of the real cost hides — often the largest line item, not the modelling.
Highest impactFoundation-model API vs fine-tuning vs a model trained from scratch. Each step up adds capability and control — and cost, both to build and to run.
High impactA standalone tool is cheap. Wiring AI into your CRM, ERP, auth, and existing workflows — safely — is where engineering hours accumulate.
High impact“Good enough” is affordable. “Cannot be wrong” or “must answer in 200ms” means more evaluation, redundancy, and testing — and a bigger budget.
Medium–highHIPAA, SOC 2, GDPR, on-prem or VPC deployment, audit trails. Regulated builds carry real overhead — but it’s predictable when scoped early.
MediumA model isn’t “done” at launch. Monitoring, drift detection, and retraining are a running cost — budget roughly 15–25% of build cost per year.
OngoingTwo companies asking for “an AI assistant” can get quotes 4× apart — honestly. Here’s what accounts for the gap, ordered by how much it moves the number.
The single biggest swing. Clean, labelled, accessible data can halve a build; messy or scattered data is where most of the real cost hides — often the largest line item, not the modelling.
Foundation-model API vs fine-tuning vs a model trained from scratch. Each step up adds capability and control — and cost, both to build and to run.
A standalone tool is cheap. Wiring AI into your CRM, ERP, auth, and existing workflows — safely — is where engineering hours accumulate.
“Good enough” is affordable. “Cannot be wrong” or “must answer in 200ms” means more evaluation, redundancy, and testing — and a bigger budget.
HIPAA, SOC 2, GDPR, on-prem or VPC deployment, audit trails. Regulated builds carry real overhead — but it’s predictable when scoped early.
A model isn’t “done” at launch. Monitoring, drift detection, and retraining are a running cost — budget roughly 15–25% of build cost per year.
Enabled users to retrieve operational, financial, and project insights through natural language queries, transforming complex data analysis into instant, self-service intelligence.
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<12 Seconds 
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Fixed-Price PoCValidate an AI use case with a low-risk, fixed-scope engagement designed to prove value, feasibility, and ROI before committing to a full build.
4–6 week delivery timeline Defined scope & success criteria Low commitment, fixed budget ($12k–20k) Executive-ready ROI assessment
Embedded Engineering PodA cross-functional AI team embedded into your environment — working within your processes, security, and tools, month to month as scope evolves.
AI, Data & MLOps specialists Named delivery lead Works within your NDA, VPN & MSA Scalable team composition
Project-BasedEnd-to-end delivery of a defined AI capability with fixed scope, timeline, and commercial terms. Full knowledge transfer and documentation included.
Fixed scope & pricing Defined milestones & deliverables Dedicated project management Knowledge transfer & documentationLet's discuss the right engagement model for your project?
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First-hand experiences from firms that put machine learning to work and achieved measurable results.
View all client testimonials“Vocso team has really creative folks and is very co-operative to implement client project expectations. MicroSave Consulting had great experience working with Anju and Prem.”
“Working with Deepak and his team at Vocso is always a pleasure. They employ talented staff and deliver professional quality work every time.”
“We love how our website turned out! Thank you so much VOCSO Digital Agency for all your hard work and dedication.”
“VOCSO SEO & SEM services helped me find new customers in a small budget. Their advanced SEO strategies made us visible to everyone.”
“Vocso team has really creative folks and is very co-operative to implement client project expectations. MicroSave Consulting had great experience working with Anju and Prem.”
“Working with Deepak and his team at Vocso is always a pleasure. They employ talented staff and deliver professional quality work every time.”
“We love how our website turned out! Thank you so much VOCSO Digital Agency for all your hard work and dedication.”
“VOCSO SEO & SEM services helped me find new customers in a small budget. Their advanced SEO strategies made us visible to everyone.”
Default Tool Description
Transparency on every decision Talented Team of AI Engineers Smooth Collaboration & Reporting Efficient & Adaptive Workflow Strict Privacy Assurance with NDA 12 Months Free Post-Launch Support On-time Delivery, No Surprises ISO 27001 Certified EngineeringMost teams start with one high-value model — churn, demand forecasting, or an anomaly detector — on data they already have. We help you scope, build, and prove it in 6 weeks, with the result measured against your current baseline. No open-ended contracts. No ambiguous scope.
deepak@vocso.com — no forms, no funnels.
For most companies, a single focused model — churn, forecasting, scoring, or a RAG assistant — runs $20,000–$60,000, while a broader build with data pipelines, full MLOps, and monitoring runs $60,000–$150,000+. A proof of concept to validate the idea on your real data starts at $12,000–$20,000. The biggest variables are your data readiness and how deeply the system integrates with your existing tools, so every engagement opens with a free 30-minute discovery call before any number is fixed.
Mostly data readiness and integration depth. A clean-data, standalone tool is a fraction of the cost of one trained on messy data and wired into your CRM, authentication, and compliance requirements. Two companies can ask for “an AI assistant” and get quotes 4× apart for exactly these reasons. That is why we scope a short discovery step before quoting — it usually saves money and removes surprises later.
A fixed-price proof of concept that targets the single biggest unknown. For $12,000–$20,000 over about six weeks, you validate feasibility on your real data, get concrete cost data for the full build, and avoid over-committing budget to something unproven. It is the lowest-risk way to turn an idea into a costed, evidence-based plan.
Upfront, almost always yes — an API-based build ships faster and cheaper. But at high call volumes the per-request cost adds up, and a fine-tuned or custom model can be cheaper to run long-term, as well as more private and controllable. We model both your build cost and your ongoing run cost before you commit, so the decision is based on your actual usage rather than a rule of thumb.
Plan for roughly 15–25% of the build cost per year. That covers model or API inference, cloud infrastructure, monitoring, and periodic retraining as your data and the world shift. A model degrades quietly rather than failing loudly, so this ongoing care is what keeps its predictions trustworthy — we define operational ownership and running cost before go-live, not after.
Significantly. Clean, labelled, accessible data lowers cost and shortens the timeline. Sparse, messy, or unlabelled data means more data engineering and labelling work — often the single largest line item in a project, ahead of the modelling itself. We assess your data in discovery so this is scoped honestly upfront and there are no surprises mid-build.
Beyond the build, budget for model or API inference (which scales with usage), cloud infrastructure, monitoring and alerting, and periodic retraining. For regulated builds, add compliance and security overhead. None of these are surprises when scoped early — we lay out both the one-time build cost and the recurring run cost so the total cost of ownership is clear from day one.
Yes. Once scope is clear — usually after a short discovery sprint — we offer milestone-based fixed pricing so you have budget certainty. For evolving scope where requirements will change as you learn, an embedded engineering pod or staff-augmentation model fits better and keeps you flexible month to month.
A scoped proof of concept runs in about six weeks. A production system typically takes 10–16 weeks: roughly 2 weeks discovery and data assessment, 3–5 weeks data preparation and modelling, 3–4 weeks deployment and MLOps, then evaluation, pilot, and production. The biggest variable is data readiness, which we scope upfront so the timeline — and the cost tied to it — is honest.
A free 30-minute strategy call to understand the problem, your data, and your constraints — then a written scope and estimate. There's no obligation, and you keep the plan whether or not you build with us. It's the fastest way to replace a range with a real number for your project.
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