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AI Prototyping
Services
Custom AI chatbot development with GPT-4, NLP, and RAG. Built for your workflows, not templates. Book a free consultation with GroupBWT today.
100+
software engineers
15+
years industry experience
$1 - 100 bln
working with clients having
Fortune 500
clients served
We are trusted by global market leaders
Who Needs AI Prototyping?
Most teams know they should be testing AI ideas before committing serious budget. Interesting concept and working demo are separated by a gap that ruins more projects than bad timing or tight budgets ever will.
These are the teams that hire us for AI prototyping services — and what made them stop trying to build it all internally.
Product Teams
You’ve got a feature hypothesis—maybe an AI copilot in your Software-as-a-Service (SaaS) product, or a recommendation engine for your marketplace. The board wants proof before allocating a quarter of engineering capacity. A consulting firm came to us because their internal dev teams were too slow for client pitches; they needed a clickable AI demo in a week, not a PowerPoint. A working prototype with actual user flows and real backend responses gets you a go/no-go decision in 2–3 weeks instead of 2–3 months.
Operations & Service Organizations
Call centers, logistics desks, compliance departments—these teams drown in repetitive decisions that an AI agent could handle at a fraction of the cost. But “AI agent” is abstract until it answers a real ticket or routes a real claim. A banking group in the Middle East had data spread across registries and financial platforms, but no tool that turned it into a clear lending signal; we built a live scoring prototype that cut SME screening from days to minutes. We prototype copilots and workflow agents tied to operational data so you can measure changes in cycle time, error rate, and throughput before committing to a full rollout.
Data and Machine Learning Teams
Your data scientists have promising models on their laptops. Getting those models into a stakeholder-friendly demo with proper data pipelines, edge-case handling, and latency constraints is a different story entirely. We bridge that gap: turning notebook experiments into interactive prototypes that test performance on production-quality inputs. A government organization identified ~50 AI use cases but had no budget; we built prototypes on synthetic data so leadership could see the value before investing in full-scope data engineering. The model that scored 94% accuracy in a Jupyter cell might drop to 78% on production-quality inputs—better to discover that at the prototype stage.
Government & Public Sector Teams
Multiple ministries commissioning the same consulting studies and paying for identical work twice—it’s a common procurement problem that stays invisible without the right tooling. We were brought in by the national government to find duplicate spending buried in thousands of historical tenders. The AI prototype caught overlapping scopes that manual review had missed for years, and it ended up as the centerpiece of a senior-level presentation. Once leadership saw actual budget savings on their screens — real numbers, not projections — the question stopped being “should we invest in AI?” and became “how soon can we roll this out?”
Your AI Idea Needs More Than Slides
We build a clickable, data-connected prototype that stakeholders can test themselves. If the concept holds up, you walk into the next meeting with proof.
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AI Prototyping Services —
Core Technologies
AI Models & Application Logic
Most prototypes live or die on model selection. We test 2–3 candidates per project against project-specific data and pick the one that hits the best accuracy-to-cost ratio for your specific use case.
The application layer connects models to your data through retrieval pipelines, agent orchestration, and structured output parsing. LangChain and LlamaIndex handle the heavy lifting; FastAPI serves the results.
Models & Frameworks
Python · LangChain · LlamaIndex · Scikit-learn · Pandas · FastAPI · Node.js · OpenAI (GPT-4o, GPT-4 Turbo) · Anthropic (Claude 3.5) · Google Gemini · Llama 3 · Mistral · DeepSeek
Data Storage & Retrieval
If you’re building a RAG-based prototype, the vector search layer has to return relevant context fast enough that the user doesn’t notice a delay. We pick the database based on what your project actually needs. Pinecone, when you want managed infrastructure and don’t want to think about scaling. ChromaDB for quick local experiments where you’re still figuring out what works. Weaviate or Elasticsearch if you need hybrid keyword + semantic search, which comes up more often than people expect.
On the structured data side, PostgreSQL and Redis sit alongside the vector stores. Caching, session state, relational queries — the stuff LLMs can’t handle on their own.
Vector & Traditional Databases
Pinecone · ChromaDB · Weaviate · Elasticsearch · Redis · PostgreSQL
Cloud, Interfaces & Data Pipelines
Beyond the model itself, a prototype needs a way to compute, a way for people to interact with it, and clean data flowing in. We run on AWS, GCP, or Azure — whichever your team already uses. No vendor lock-in. No surprise migration projects.
Stakeholder demos usually go out as Streamlit or Gradio apps because they’re fast to spin up. When the prototype is client-facing, we build a proper React or Next.js frontend instead. Data pipelines use Airflow and dbt to keep inputs fresh and reproducible.
Cloud & Tooling
AWS (SageMaker, Bedrock) · GCP (Vertex AI) · Azure AI · Hugging Face · MLflow · Streamlit · Gradio · Next.js · React · Apache Airflow · dbt · Spark · Custom ETL pipelines
Types of AI Prototypes We Build
01.
LLM Apps & Copilots
Chatbots and RAG-powered search tools. Domain-specific assistants built for summarization, content generation, or knowledge retrieval — sometimes all three, sometimes just one. We use retrieval-augmented generation patterns, so responses stay grounded in your data—not hallucinated from training sets.
02.
Autonomous & Workflow Agents
These are AI systems that don’t wait for instructions on every step. They plan, reason, and execute across tools and APIs on their own. Think scheduling agents that rebook meetings when conflicts arise, or data pipeline orchestrators that notice a failed job and fix it before your team wakes up. If it requires an AI to chain decisions and actions without someone babysitting the process, that’s what we prototype.
03.
Computer Vision & Speech
OCR pipelines for messy scanned documents. Image classification that actually works on your specific inventory photos, not just ImageNet benchmarks. Automated captioning, speech-to-text, text-to-speech — we’ve built all of these. What matters to us is that the vision and speech modules plug into your existing workflows.
04.
Evaluation, Guardrails & Routing
LLM evaluators, fact-checking layers, quality guardrails, routing engines, ranking models, and lightweight recommendation systems. Critical for any production system where wrong answers carry real cost—financial, legal, or reputational—we build prototypes that prove the logic works on your data.
Our AI Prototyping Process
Every prototype follows five phases. Each one produces a distinct output—so you always know what’s been done and what comes next.
01/05
01/05
How to Start Working with
GroupBWT for AI Prototyping
Fixed scope. Clear output. You define the goal, we ship a working prototype with actual user flows and backend responses that your team can click through—and get a go/no-go decision in 2–3 weeks instead of 2–3 months.
Prototype-as-a-Service
This model fits consulting firms running fast pitch cycles especially well: we deliver a functional AI demo with production-ready architecture, and your team walks into the client meeting with a working product. Pitch wins? We become the implementation partner.
Dedicated AI Team
A combined team—ML engineers, data engineers, designers—aligned with your plan. Continuous development from prototype through production. Best for organizations running multiple AI experiments or transitioning prototypes into full-scope products.
Staff Augmentation
Need a senior ML engineer for three months? A data scientist to run experiments alongside your team? We embed specialists into your workflows, tools, and cadence. They operate as part of your team, not as external consultants watching from the sideline.
Consulting & Advisory
Expert support before the build. We help you define your AI vision, evaluate vendor options, assess data readiness, and create a prioritized plan. Best for executives who need clarity from an expirienced AI prototype development company.
Our Cases
Our partnerships and awards
What Our Clients Say
What do you like best?
Their deep understanding of our needs and how to craft a solution that provides more opportunities for managing our data. Their data solution, enhanced with AI features, allows us to easily manage diverse data sources and quickly get actionable insights from data.
What do you dislike?
It took some time to align the a multi-source data scraping platform functionality with our specific workflows. But we quickly adapted and the final result fully met our requirements.
What do you like best?
It was incredible how they could build precisely what we wanted. They were genuine experts in data scraping; project management was also great, and each phase of the project was on time, with quick feedback.
What do you dislike?
We have no comments on the work performed.
What do you like best?
GroupBWT is the preferred choice for competitive intelligence through complex data extraction. Their approach, technical skills, and customization options make them valuable partners. Nevertheless, be prepared to invest time in initial solution development.
What do you dislike?
GroupBWT provided us with a solution to collect real-time data on competitor micro-mobility services so we could monitor vehicle availability and locations. This data has given us a clear view of the market in specific areas, allowing us to refine our operational strategy and stay competitive.
What do you like best?
The company's dedication to understanding our needs for collecting competitor data was exemplary. Their methodology for extracting complex data sets was methodical and precise. What impressed me most was their adaptability and collaboration with our team, ensuring the data was relevant and actionable for our market analysis.
What do you dislike?
Finding a downside is challenging, as they consistently met our expectations and provided timely updates. If anything, I would have appreciated an even more detailed roadmap at the project's outset. However, this didn't hamper our overall experience.
What do you like best?
GroupBWT excels at providing tailored data scraping solutions perfectly suited to our specific needs for competitor analysis and market research. The flexibility of the platform they created allows us to track a wide range of data, from price changes to product modifications and customer reviews, making it a great fit for our needs. This high level of personalization delivers timely, valuable insights that enable us to stay competitive and make proactive decisions
What do you dislike?
Given the complexity and customization of our project, we later decided that we needed a few additional sources after the project had started.
What do you like best?
What we liked most was how GroupBWT created a flexible system that efficiently handles large amounts of data. Their innovative technology and expertise helped us quickly understand market trends and make smarter decisions
What do you dislike?
The entire process was easy and fast, so there were no downsides
What do you like best?
Their deep understanding of our needs and how to craft a solution that provides more opportunities for managing our data. Their data solution, enhanced with AI features, allows us to easily manage diverse data sources and quickly get actionable insights from data.
What do you dislike?
It took some time to align the a multi-source data scraping platform functionality with our specific workflows. But we quickly adapted and the final result fully met our requirements.
What do you like best?
It was incredible how they could build precisely what we wanted. They were genuine experts in data scraping; project management was also great, and each phase of the project was on time, with quick feedback.
What do you dislike?
We have no comments on the work performed.
What do you like best?
GroupBWT is the preferred choice for competitive intelligence through complex data extraction. Their approach, technical skills, and customization options make them valuable partners. Nevertheless, be prepared to invest time in initial solution development.
What do you dislike?
GroupBWT provided us with a solution to collect real-time data on competitor micro-mobility services so we could monitor vehicle availability and locations. This data has given us a clear view of the market in specific areas, allowing us to refine our operational strategy and stay competitive.
What do you like best?
The company's dedication to understanding our needs for collecting competitor data was exemplary. Their methodology for extracting complex data sets was methodical and precise. What impressed me most was their adaptability and collaboration with our team, ensuring the data was relevant and actionable for our market analysis.
What do you dislike?
Finding a downside is challenging, as they consistently met our expectations and provided timely updates. If anything, I would have appreciated an even more detailed roadmap at the project's outset. However, this didn't hamper our overall experience.
What do you like best?
GroupBWT excels at providing tailored data scraping solutions perfectly suited to our specific needs for competitor analysis and market research. The flexibility of the platform they created allows us to track a wide range of data, from price changes to product modifications and customer reviews, making it a great fit for our needs. This high level of personalization delivers timely, valuable insights that enable us to stay competitive and make proactive decisions
What do you dislike?
Given the complexity and customization of our project, we later decided that we needed a few additional sources after the project had started.
What do you like best?
What we liked most was how GroupBWT created a flexible system that efficiently handles large amounts of data. Their innovative technology and expertise helped us quickly understand market trends and make smarter decisions
What do you dislike?
The entire process was easy and fast, so there were no downsides
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FAQ
How long does it take to build an AI prototype?
Two to four weeks covers most projects, from the first discovery call through a working interactive demo. If it’s a simpler PoC (Proof of Concept) — say, a single LLM connection to an existing dataset — we’ve turned those around in 5–7 business days. Larger builds are a different story. Multiple data sources, custom model training, multi-agent architectures — those tend to land in the 3–4 week range. The biggest variables are how clean your data is when we start, how complex the scope gets, and whether your stakeholders are available for feedback along the way.
What's the difference between a prototype and an MVP?
A prototype answers one question: Can this AI approach actually solve the problem with your data? An MVP (Minimum Viable Product) answers a different question: will people pay for it? Because prototypes focus on the core AI logic, we skip production-grade authentication, billing, user signup flows, and growth-stage compute. That’s what makes them faster and cheaper. Many clients start with a prototype, then move to MVP development once the AI concept is proven.
How much do AI prototype development services cost?
Pricing depends on scope. A focused PoC with a single LLM connection and existing data typically runs $8K–$20K. Multi-component prototypes involving RAG systems, custom agents, or vision pipelines range from $20K–$50K. After the discovery session, you get a fixed-price quote. No hourly billing. If scope changes, we talk about it first — nothing gets added without your sign-off.
Can the prototype grow into a production system?
That’s the whole point. We structure prototype code so it doesn’t need a rewrite if you move forward — clean separation, documented APIs, and clear model choices. Going to production means layering on the things we deliberately left out: auth, monitoring, and deployment automation. The core logic carries over.
What industries benefit most from the rapid prototyping services for AI ideas?
Financial services is a big one — fraud detection, Know Your Customer automation, and credit scoring. We’ve built live SME scoring tools for Middle East banks, so this isn’t hypothetical for us. Healthcare comes up often, too, especially clinical Natural Language Processing and diagnostic triage. Government and public sector teams bring us in for things like duplicate tender detection and budget optimization. We’ve also done contract analysis for legal teams, route calculation and demand prediction for logistics companies, and client-facing AI demos for consulting firms running pitch cycles. The common thread? Any team sitting on structured or semi-structured data and wondering whether AI could improve a specific workflow. The prototype gives them a real answer backed by their own data.
You have an idea?
We handle all the rest.
How can we help you?
