AI Development Company
A convincing AI prototype does not prove the product will handle messy data, changing models, rising usage, or a customer asking the question nobody tested. RaftLabs turns a defined AI use case or existing prototype into a production system with evaluation, guardrails, monitoring, and cost controls.
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The problem
Does the prototype work only on the examples used to make the demo?
Can anyone explain output quality, failure handling, and cost at your expected volume?
Short answer
An AI development company turns a defined use case into software that can be tested, monitored, and operated. RaftLabs develops RAG, agent, ML, and document AI systems. A proof of concept starts at $9,500, with success criteria agreed before development.
A demo gets ten clean examples and an attentive room. A product gets incomplete records, ambiguous requests, impatient users, provider outages, and a model bill that grows with every success.
AI development is the work between those two moments. RaftLabs keeps what the prototype proved, then builds the evaluation, product workflow, integration, and operating controls required for real use.
Proof
AI is a fit when the uncertainty can be tested. It is not a substitute for an undefined product or an ownerless decision.
A user has a repeated task, decision, or information gap that current software handles poorly.
You can provide representative inputs and an existing baseline for quality, time, or cost.
A named owner can approve failure rules, human review, and the first release boundary.
Scope
| Off-the-shelf AI | Custom AI system | |
|---|---|---|
| Best when | The task and workflow are common | The workflow, data, or customer experience is specific |
| Time to value | Days or weeks when configuration is enough | Weeks or months after feasibility is proved |
| Control | Limited to vendor settings and interfaces | Designed around your permissions, review, and failure rules |
| Ownership | Licence access to a vendor product | Ownership of delivered code and agreed project IP |
| First test | Trial with real users and data | Time-boxed proof against a written success threshold |
Buy when a product solves the task cleanly. Develop when an important workflow or product advantage cannot be reached through configuration. We make that call before recommending a larger phase.
How it works
Every phase answers a decision. A phase that fails its threshold should stop the spend.
Name the user, the exact task, today's baseline, representative inputs, and the cost of a wrong result. Set a measurable threshold for quality, latency, and running cost before selecting a model.
Test the smallest viable approach on data that includes ordinary cases and awkward ones. Record where it fails. The output is evidence and a recommendation, not a demo dressed as a product.
Add the product workflow around the chosen approach: permissions, integrations, evaluation, monitoring, cost limits, and human review. Model changes must pass the same test set before they reach users.
Start with a controlled user group or workload. Compare live results with the baseline and watch failure, escalation, latency, and cost. Expand only after the named owner accepts the evidence.
Scope and price
A focused proof of concept starts with one use case, representative data, a baseline, and a written pass threshold.
A proof of concept usually takes 3 to 6 weeks. A production phase is priced after feasibility and operating requirements are clear.
If feasibility is already proven, we can scope the first production workflow directly rather than repeat discovery you have already done.
A real stop decision
If the proof misses the agreed threshold, we document why and do not turn uncertainty into a larger build.
Fixed-price phase
Each agreed phase has written deliverables, acceptance criteria, and price. Scope changes are approved before work starts.
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Read moreAn AI development company designs the software, data flows, model interactions, evaluation, and operating controls around an AI use case. The work can include generative AI, retrieval, agents, machine learning, computer vision, voice, or document processing. The right category depends on the job, data, and acceptable failure.
Use generative AI when the system must create or transform content, an AI agent when it must take multi-step actions, machine learning when it must predict or classify from historical data, and intelligent document processing when documents are the main input. The AI development page is the umbrella for buyers who have not chosen a path.
Start with a proof of concept when model quality, data readiness, latency, or running cost is genuinely uncertain. A useful proof has representative inputs, a baseline, a pass threshold, and a stop decision. A polished demo without those four things does not reduce project risk.
A focused proof of concept starts at $9,500. A production AI feature commonly starts around $25,000 and grows with integrations, data work, evaluation depth, user roles, and operating controls. RaftLabs scopes each phase and fixes its price before development starts.
Clients own the delivered application code, prompts, evaluation assets, and agreed project IP. Third-party models remain subject to their providers' terms. Model and cloud usage should run through client-owned accounts where practical, so the buyer sees the operating cost directly.
Work with us
We will identify the smallest test that can tell you whether the idea deserves a production build.