What a realistic MVP budget looks like in 2026
The line items that decide the cost of a first release, and the ones founders usually leave out.
Product engineering
AI and GenAI,
built for production.
LLM features, search over your documents and workflow automation, built on your own data with evaluation and guardrails. For companies ready to take AI past the demo.
10 yearsBuilding software
250+Projects delivered
90%Client retention
NDA before we talk
Your data never trains public models
“They work like part of our own team. Same standups, same board, and they push back when a decision is wrong.”
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Trusted by teams in the US, UK, Canada & Europe
What we build
Six kinds of AI work we take on most
Most projects start with one use case on one set of data. Once it proves itself, the same pipelines and guardrails carry the next one.
01
Document Q&A and search
Retrieval over your policies, contracts, manuals or tickets, with answers that cite the source.
For exampleAn assistant that answers support agents from 4,000 help articles
02
LLM features in your product
Summaries, drafting, classification and chat inside your app, with a cost and speed budget for each request.
For exampleAuto-drafted replies inside a customer support tool
03
Workflow automation and agents
Models that read emails, PDFs and forms, then update your CRM, ERP or ticketing system, with a person approving where it matters.
For exampleSupplier invoices read and entered into the accounting system
04
Data preparation and pipelines
Cleaning, splitting and syncing the data your AI depends on, so answers stay current as documents change.
For exampleA nightly sync from SharePoint into a search index
05
Evaluation and guardrails
Test sets, accuracy scoring, personal data filtering and fallbacks, so you know how well it works before users do.
For exampleA 300-question test set run on every prompt change
06
Monitoring and cost control
Accuracy, speed and spend tracked in production, with model routing to keep the monthly bill predictable.
For exampleSimple requests routed to a smaller, cheaper model
Why Leapstrat
Why businesses choose Leapstrat for AI
An off-the-shelf AI tool compared with an AI build from Leapstrat. A ready-made tool is often the right start; we help you see where it stops fitting.
Compare
Off-the-shelf AI tool
Leapstrat AI build
Your dataLimited connectors, data leaves your systemsConnected to your systems, hosted where you choose
AccuracyHard to measure on your own questionsScored against a test set before launch
Fit to workflowYour team works around the toolBuilt into the tools your team already uses
CostPer-seat licences that grow with headcountPay for usage, with spending limits
Model choiceLocked to the vendor’s modelSwap models as prices and quality change
Who owns itThe vendorYou: code, prompts, data and accounts
01
A founder on every project
One of our two founders joins the workshop and stays on the project as your point of escalation.
02
Hours that overlap yours
Engineers on shift 09:00–23:00 IST: 4.5 hours with US Eastern, a full working day with the UK and Europe.
03
Handover planned from day one
Code, prompts, test sets and docs are kept ready for your own team to take over.
Worried about data privacy? We can run models inside your own cloud account and sign a DPA.
How we handle your data →How a project runs
A pilot on your data in 8 weeks, live in 12
We prove the use case on a sample of your data before any larger build, and you see accuracy scores at every step. The bar shows a typical project, week by week.
011–2 weeks
Feasibility check
We test the use case on a sample of your data and check accuracy, cost per request and data access.
You get: Go or no-go report and estimate02About 2 weeks
Data and test set
Data is cleaned and connected, and we write the questions the system must answer correctly.
You get: Data pipeline and evaluation set03About 4 weeks
Pilot build
A working version used by a small group, with accuracy scored at the end of every sprint.
You get: A pilot with real users04About 4 weeks
Production rollout
Guardrails, access control, monitoring and spending limits, then a staged rollout to all users.
You get: Live system and runbook05Ongoing
Monitor and improve
Accuracy and cost are reviewed every month, and prompts, data or models are updated as needed.
You get: A monthly accuracy and cost report
Every sprint:
A demo call with the engineers
Accuracy scores on the test set
Cost per request and response time
An updated estimate if scope changes
Ways to work with us
Pick how the AI is built
Most clients start with a fixed scope pilot, then move to a dedicated pod to run and extend it. You can switch between phases.
Fixed scope projectA pilot with a clear finishMost pilots
Dedicated podA team that runs and extends it
Staff augmentationAI engineers inside your team
Best for
Feasibility checks and pilots
AI in production with a roadmap
Teams with a data or engineering lead
Pricing
Fixed price per phase
Monthly rate for the whole pod
Flat monthly rate per engineer
Led by
Our delivery lead, against milestones
Our delivery lead, reporting to you
Your own engineering lead
Team
AI engineer, data engineer and QA, set by the scope
Lead, AI and data engineers, QA
From one engineer, month to month
How it works →
How it works →
How it works →
Fixed scope project
A pilot with a clear finish
Most pilots- Best for
- Feasibility checks and pilots
- Pricing
- Fixed price per phase
- Led by
- Our delivery lead, against milestones
- Team
- AI engineer, data engineer and QA, set by the scope
Dedicated pod
A team that runs and extends it
- Best for
- AI in production with a roadmap
- Pricing
- Monthly rate for the whole pod
- Led by
- Our delivery lead, reporting to you
- Team
- Lead, AI and data engineers, QA
Staff augmentation
AI engineers inside your team
- Best for
- Teams with a data or engineering lead
- Pricing
- Flat monthly rate per engineer
- Led by
- Your own engineering lead
- Team
- From one engineer, month to month
Client result · Retail and ecommerce · Europe
12→1
Days of manual reporting, per monthly cycle
Monthly reporting, cut from twelve days to one
ChallengeAnalysts spent twelve days a month pulling sales and stock data from five systems into board reports.
ApproachA data pipeline into one warehouse, then an LLM that drafts the commentary for analysts to check.
ResultReports ready in one day each cycle, with analysts reviewing figures instead of copying them.
PythonOpenAIPostgreSQLAirflow
See all client results →
Not sure AI is the right fit?
Bring the use case and a sample of the data. On a 30-minute call we will tell you whether AI fits, where to start and what it should cost to run.
Industries and technologies
AI for seven sectors, on models you can swap later
The sector sets the rules for privacy, audit trails and human review. We keep the model layer separate, so you can change providers as prices and quality move.
Industries
SaaS and B2BFintechHealthcareLegal and professional servicesRetail and ecommerceLogisticsTravel Sector not listed? Book a call →
ModelsOpenAI · Anthropic Claude · Google Gemini · Llama · Mistral
FrameworksLangChain · LlamaIndex · Hugging Face · MLflow
Data and retrievalPython · PostgreSQL + pgvector · Pinecone · Elasticsearch · Airflow
Cloud and operationsAWS Bedrock · Azure OpenAI · Docker · Langfuse · CI/CD
Security and ownership
✓ NDA signed before discovery✓ Your data never used to train public models✓ Hosted in your own cloud account if required✓ PII filtering and audit logs
FAQ
Questions about AI and GenAI development
Data, accuracy and running costs. Anything else, ask on the call.
Most pilots fall between $20k–$50k. It depends on the data, the integrations and how accurate the system must be. Running costs are estimated per request up front. After a 30-minute call we send a written estimate within one working day.
No. We use enterprise APIs that do not train on your data, or open models hosted in your own cloud account. Data access is agreed in writing before the feasibility check.
Answers are grounded in your documents and cite the source. We score accuracy against a test set before launch, add guardrails and fallbacks, and route uncertain cases to a person.
The one that fits the task, the budget and your privacy needs: OpenAI, Claude, Gemini or an open model such as Llama. The model layer is kept separate so you can switch later.
We estimate the cost per request during the feasibility check and set spending limits in production. Model routing sends simple requests to smaller, cheaper models.
Yes. A two-week audit tests your current set-up against real questions, then we send a fix-first list covering data, retrieval, prompts and cost.
Insights
All AI articles →
Read before you start
Hiring
Dedicated team or fixed-price project: how to choose for your next build
Most software engagements fail the brief before anyone writes code, because the commercial model does not match how the work will actually change. A dedicated team and a fixed-price project are both valid. They answer different questions. Choose a dedicated team when the roadmap will move A dedicated team is the right shape when you […]
Engineering
Shipping an MVP in 12 weeks with a distributed team
Twelve weeks is enough to put a product in front of real users. It is not enough to build the version you will be embarrassed to throw away later if the first month is spent negotiating the backlog. Week 0 is a decision, not a workshop Before the first sprint, name the user, the job […]
Tell us what AI should do for you.
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