Build Applications That Learn and Adapt
Artificial intelligence is not a product you buy - it is a capability you build into the right places in your product. Jspace integrates AI and ML features that solve specific, measurable problems: automating repetitive decisions, extracting insight from unstructured data, or giving users an intelligent interface that adapts to how they work. We connect your product to LLMs, train custom models where needed, and build the infrastructure that keeps those models reliable in production.
What we focus on
LLM integration and prompt engineering
We integrate OpenAI, Anthropic, and open-source models into your product with structured outputs, context management, and cost controls that make LLM features reliable at scale.
Intelligent automation pipelines
We replace manual, rule-based processes with ML-driven workflows that classify, route, and act on data without human intervention - and flag edge cases for review.
Predictive analytics and recommendation engines
We build models that forecast demand, detect anomalies, or surface personalized recommendations using your historical data and retraining pipelines that keep them accurate over time.
Responsible AI and production reliability
We instrument AI features with evaluation frameworks, fallback logic, and human-in-the-loop checkpoints so your product behaves predictably even when model outputs surprise you.
Key capabilities
- OpenAI, Anthropic, and open-source LLM integration
- Retrieval-augmented generation (RAG) with vector databases
- Custom ML model training and fine-tuning
- AI-powered document processing and data extraction
- Intelligent chatbots and conversational UI
- ML model monitoring, drift detection, and retraining
Applications we build
Customer support chatbot
SMBs with support teams handling repetitive incoming questions about orders, product specs, or account information can deflect a large share of that volume to a context-aware chatbot trained on their own documentation and product data, freeing staff for higher-value interactions.
Document and invoice automation
Operations-heavy businesses processing large volumes of supplier invoices, contracts, or intake forms manually can use AI document processing to extract structured data, validate fields, and route documents automatically - reducing processing time and error rate.
Demand forecasting
Retailers and distributors that rely on intuition or static formulas to plan stock levels can replace that with an ML forecasting model that learns from historical sales, seasonality patterns, and external signals to generate more accurate replenishment recommendations.
Content and text generation pipeline
E-commerce businesses maintaining large product catalogues struggle to write consistent, keyword-rich product descriptions at scale. An LLM-powered generation pipeline can produce first-draft descriptions from product attributes and allow editors to review and publish, reducing content production time substantially.
Frequently asked questions
Can you add AI features to our existing product rather than build something new?
Yes - the majority of our AI projects are integrations into existing products, not greenfield builds. We identify the specific workflows where AI adds measurable value, integrate via API or embedded model, and instrument the feature so you can monitor its accuracy and business impact over time.
What is the difference between using an LLM API and training a custom model?
LLM APIs like OpenAI or Anthropic give you a powerful general-purpose language model that you prompt and orchestrate - fast to integrate and effective for a broad range of language tasks. Custom model training makes sense when you have proprietary data that gives you a genuine advantage over general models, or when latency and cost at scale make a smaller fine-tuned model preferable. We assess which approach is right for your use case in the discovery phase.
How do you make sure AI features behave reliably in production?
We instrument every AI feature with evaluation pipelines that test outputs against expected results, alerting when accuracy degrades. For LLM features we use structured outputs, validation layers, and fallback logic so the application handles unexpected model responses gracefully rather than surfacing them to the user.
How long does it take to build an AI-powered feature?
A focused AI integration such as a document extraction pipeline or a chatbot with a defined knowledge base typically takes 6 to 10 weeks including evaluation and production hardening. Projects requiring custom model training or complex orchestration take longer. We scope each project individually after understanding your data and requirements.
Is our data safe when we use third-party AI models?
We configure API usage to opt out of training data sharing where providers allow it, and we advise on which data should stay on-premise versus what can safely pass through third-party APIs. For sensitive use cases we evaluate open-source models that run entirely within your own infrastructure.
Ready to add intelligence to your product?
Tell us the problem you want AI to solve and we'll recommend the right approach - no buzzwords, just practical engineering.
Start your project