Smart product recommendations, chatbots, data pipelines, and ML-powered features baked into your product from day one. Not demos — deployed, production-ready AI.
What We Build
We build AI features that go to production — not prototypes that sit in a notebook.
Intelligent, context-aware chatbots powered by LLMs like GPT-4 and Claude. Trained on your data, embedded in your product, and built to handle real customer conversations.
Collaborative filtering, content-based, and hybrid recommendation systems. Drive product discovery, content engagement, or personalised user journeys at scale.
End-to-end pipelines that ingest raw data, clean it, transform it, and surface insights in real time. From CSV exports to live streaming data — we handle both.
Classifiers, regression models, NLP pipelines, and computer vision systems trained on your specific domain data. Built to outperform off-the-shelf solutions.
Vector-based search that understands meaning, not just keywords. Users find exactly what they're looking for — even when they don't use the exact right words.
Extract structured data from unstructured documents — contracts, invoices, forms, and reports — using OCR and NLP. Automate manual review workflows entirely.
Churn prediction, demand forecasting, fraud detection, lead scoring. ML models that turn your historical data into forward-looking business intelligence.
Automate repetitive tasks using AI agents — content classification, email triage, data enrichment, report generation. Your team focuses on what only humans can do.
Integration Approach
No ripping and replacing. We add AI to your existing product, stack, and infrastructure — without disrupting what already works.
We expose AI features as clean REST or GraphQL APIs that slot directly into your existing frontend or backend — no architectural overhaul required.
We fine-tune models on your proprietary data. Nothing is sent to third-party training pipelines — your data stays yours, your models stay yours.
Deploy on AWS, GCP, Azure, or your own infrastructure. We containerise everything with Docker and Kubernetes for portability and scale.
Models degrade over time. We set up monitoring dashboards, performance alerts, and automated retraining triggers so your AI stays accurate.
Every LLM integration ships with output filtering, prompt injection protection, rate limiting, and content safety layers. Responsible AI by default.
Async queues, autoscaling inference endpoints, and caching layers mean your AI feature handles 10 users and 10,000 users without re-architecture.
Who It's For
We don't build AI for the sake of a press release. We build it because it genuinely makes your product better.
Personalised product recommendations, visual search, smart sizing assistants, and demand forecasting that increase basket size and reduce returns.
Replace FAQ pages and support queues with intelligent assistants that resolve 70%+ of queries without a human in the loop.
You're sitting on months or years of transaction, behavioural, or operational data. We turn that into a competitive advantage with ML.
Document processing, data entry, report generation, and classification tasks that cost your team hours every day and can be automated with AI.
"SparkForge built a recommendation engine into our store in 4 weeks. Average order value went up 22% in the first month."
"The AI assistant they built handles 74% of our support tickets automatically. Our team finally has time for the hard stuff."
How We Work
We assess your data, define the right problem framing, and determine which AI approach will deliver the most value — before writing a single line of model code.
We build a fast PoC to validate the approach against your real data. You see actual model performance numbers — accuracy, latency, edge cases — before committing to a full build.
PoC validated — we productionise it. API wrappers, error handling, logging, autoscaling, and integration with your existing product. Built to handle real load.
Post-launch, we monitor model performance, collect feedback signals, and retrain on new data. AI that gets smarter over time, not one that quietly drifts.
Technology
We use proven tools — not every shiny new framework. Your AI runs on infrastructure you can trust.
Pricing
AI projects vary enormously in scope. Here's a starting framework — your quote will be specific to your use case.
A single AI-powered feature integrated into an existing product — chatbot, search, or a simple classifier.
A complete AI-powered product feature — custom trained, integrated, monitored, and production-deployed.
Multiple AI systems working together — recommendations, NLP, analytics, and automation at enterprise scale.
All prices are indicative. Final quotes depend on data complexity, model type, and integration scope. Get a free estimate.
FAQ
It depends on the type of AI. For LLM integrations and chatbots, you can start with very little data — we use pre-trained models and fine-tune with even small datasets. For custom ML models, we'll audit your existing data during discovery and tell you honestly if it's sufficient or what you'd need to collect.
Both — it depends on the use case. For conversational AI and document intelligence, we typically integrate with leading LLM APIs (OpenAI, Anthropic) and add your data as context via RAG. For structured prediction tasks like recommendations or churn modelling, we train custom models on your data. We always recommend the approach that gives the best results for your specific problem.
Yes. We use enterprise API agreements where customer data is not used for model training by providers. For highly sensitive data, we can deploy open-source models (Llama, Mistral) entirely on your own infrastructure so data never leaves your environment.
Every LLM integration we build includes output guardrails — content filters, hallucination detection, confidence thresholds, and fallback paths. We also implement human-in-the-loop checks for high-stakes workflows. Responsible AI isn't optional for us — it's part of the default build.
Yes — that's usually how it works. We expose AI capabilities as APIs that plug into your existing product. Your tech stack stays intact; we add a new layer on top. Most integrations don't require any changes to your existing frontend or database.
We set up model monitoring dashboards that track prediction quality, data drift, and user feedback signals. When performance degrades beyond a threshold, alerts fire and a retraining pipeline kicks off automatically. Your AI improves over time rather than silently getting worse.
Ready to Start?
Tell us your use case and we'll come back with a plan — and honest advice on what's possible.