AI Integration for Your Existing Products

AI integration means connecting large language models or ML models to your existing software so they enhance real workflows. This includes selecting the right model, designing the data pipeline that feeds it, building the API layer, and adding monitoring. The goal is measurable impact — not a proof of concept that never ships.

No. In most cases we connect AI to your existing backend via API calls, webhooks, or event streams. We design the integration to be additive, so your current features keep working while AI capabilities layer on top. A rebuild is only recommended when the legacy architecture would introduce unacceptable latency or security risks.

We work with OpenAI (GPT-4o, o3), Anthropic (Claude), Google (Gemini), and open-source models via Ollama or Hugging Face. Model selection depends on your latency requirements, cost budget, data privacy constraints, and the specific task. We benchmark multiple models against your actual use case before committing to one.

We implement retrieval-augmented generation (RAG) so the model answers from your verified data rather than its parametric memory. We also add output validation layers, confidence scoring, and human-in-the-loop checkpoints for high-stakes decisions. Every integration ships with an evaluation suite that catches regressions when models are updated.

RAG (retrieval-augmented generation) is a technique where relevant documents are fetched from a vector database and injected into the model prompt before generation. It dramatically improves accuracy for domain-specific questions. If your use case involves your own data — support docs, product catalogue, internal knowledge — you almost certainly need RAG.

A focused integration (single workflow, one model provider) typically ranges from $15,000–$40,000. Multi-workflow platforms with custom fine-tuning and full observability are $50,000–$150,000+. Ongoing model inference costs depend on usage volume and the provider you choose. We provide a detailed cost model before any contract is signed.

A single-workflow integration can be live in 4–8 weeks. More complex projects involving fine-tuning, multi-model orchestration, or enterprise security reviews take 3–5 months. We always start with a 2-week discovery phase to de-risk the estimate before committing to a full timeline.

Yes. We design integrations with data privacy as a first-class concern. This includes using Azure OpenAI or AWS Bedrock for data residency requirements, self-hosted models for air-gapped environments, and strict role-based access controls. We can also sign NDAs and DPAs before any data is shared with our team.

Scalability is built into the architecture from day one. We implement asynchronous queuing for high-volume inference tasks, caching layers for repeated queries, and provider failover to prevent single points of failure. Our monitoring dashboards make it straightforward to spot bottlenecks before they impact users.

We fine-tune when it delivers a measurable quality or cost advantage over prompt engineering alone — typically for classification tasks, domain-specific generation, or when latency from large context windows is prohibitive. We run head-to-head evaluations between fine-tuned and prompted baselines so the decision is data-driven.

We define success metrics before development starts: task completion rate, accuracy against a golden dataset, latency percentiles, and cost per inference. At launch we instrument every endpoint so you have live dashboards. A post-launch review at 30 and 90 days ensures the integration is delivering the business outcomes we scoped for.

We offer retainer-based support covering model updates, prompt refinement, vector index refreshes, and new feature development. We also provide a complete runbook so your internal team can handle routine maintenance independently. Monthly reports summarise model performance and flag any drift in output quality.