AI Products That Work in Production — Not Just in a Demo
Most AI prototypes never reach production. They fail on real data, real edge cases, and real security requirements. We build AI products and workflow automation engineered for your data, your compliance model, and your operational reality.
Assemble an agent from capabilities — channels, voice, knowledge, integrations, memory and autonomy level — and see the build cost, monthly running cost and delivery timeline update as you go. No email required.
| AI / LLM | OpenAI GPT-4, Anthropic Claude, Mistral, Llama 3 |
| Orchestration | LangChain, LlamaIndex, custom Python pipelines |
| Vector DB | Pinecone, pgvector, Weaviate, Qdrant |
| Backend | Python (FastAPI), Node.js |
| Model Serving | vLLM, Triton, AWS Bedrock, Azure OpenAI |
| Queue | Celery, Redis Queue, SQS |
| Storage | S3, PostgreSQL, Elasticsearch |
| Observability | LangSmith, Datadog, custom prompt tracing |
We assess your data sources, privacy requirements, and automation opportunity. Output: ranked use-case shortlist, data readiness report, and recommended model architecture.
A functional, deployed PoC on your actual data — not synthetic samples. This is where hallucination rates, latency, and cost-per-query are benchmarked against real conditions.
Full pipeline engineering: ingestion, vectorization, retrieval, generation, caching, monitoring, and API layer. Every component built for production load, not demo conditions.
Integration into your existing product or internal systems. Load testing, accuracy benchmarking, and full documentation. Your team inherits the system with complete operational runbooks.
Both. We select the model based on your data privacy requirements, latency targets, and budget. We've deployed on OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, and self-hosted Llama/Mistral depending on the compliance environment.
We configure API calls with zero data retention options, use private deployment for sensitive workloads, and never send raw company data to fine-tuning endpoints without explicit scoping.
Highly variable. We build cost monitoring dashboards into every deployment and implement semantic caching and model routing to minimize per-query costs. We'll give you a cost-per-query estimate before build begins.
Yes. We audit the existing pipeline, identify accuracy, latency, and cost problems, and propose a remediation plan. This is often faster than a full rebuild.
We design human-in-the-loop checkpoints for high-stakes decisions, implement confidence scoring to route uncertain answers for review, and use source-grounded RAG to minimize unsupported generation.