From AI strategy and data foundations to LLM copilots, computer vision and predictive ML — we build production AI that's evaluated, observable and safe. Not slides, not POCs that die on the shelf — systems that ship and stay shipped.
Ten AI service lines — from initial strategy and data audit to live production systems with evaluation, monitoring and ongoing care.
Use-case discovery, ROI modelling, build-vs-buy, vendor selection — written into a 12-month execution plan.
Domain-specific copilots and multi-step agents for engineering, support, sales and ops — with tool use, memory and audit logs.
Retrieval-Augmented Generation over your docs, drawings, contracts. Vector indexes, hybrid search, citation, re-ranking.
Defect detection, OCR for engineering drawings, AEC site-progress monitoring, asset inspection, document understanding.
Churn, propensity, demand forecasting, anomaly detection, predictive maintenance with feature stores and online inference.
Snowflake, BigQuery, Databricks, Redshift. Pipelines (Airflow, dbt), CDC, data quality, governance, lineage.
MLflow, Vertex AI, SageMaker, Azure ML. Experiment tracking, model registry, CI/CD for models, canary deploys.
Golden sets, automated metrics, hallucination tracking, red-team programmes, guardrails, citation enforcement.
LoRA / QLoRA fine-tuning of open-weight models on your domain data. Cost & latency optimisation.
Drawing search, automated drawing QA, BIM clash assistants, code-checking agents, design-precedent retrieval.
Use-case workshop, problem framing, success criteria written.
Source inventory, quality, gaps, governance, compliance.
4-6 week prototype against golden test set + UAT scenarios.
Automated + human review, hallucination tracking, cost & latency profile.
Integration, guardrails, observability, security review, deployment.
Weekly regression dashboards, monthly model reviews, drift monitoring.
Multi-modal RAG over 220k legacy CAD drawings + spec PDFs. Engineers retrieve precedent designs by natural-language query and run automated drawing QA checks in seconds. Citation-anchored answers, evaluated weekly.
OpenAI, Anthropic Claude, Google Gemini, Mistral, Llama (open-weight) and Azure OpenAI / AWS Bedrock for enterprise hosting. We help you choose based on use case, cost, data residency and your existing cloud commitments.
Retrieval-Augmented Generation grounds an LLM in your own documents. Needed whenever the answer must reference your internal data (drawings, contracts, policies, knowledge bases) — which is most enterprise use cases. Without RAG, you get generic answers; with it, you get answers grounded in your truth.
Every engagement ships with a written evaluation harness: golden test set, automated metrics (accuracy, factuality, latency, cost), human-review rubric and a regression dashboard reviewed monthly. We don't ship AI we can't measure.
We instrument guardrails (input validation, output filtering, citation requirements), red-team adversarial inputs, and ship with human-in-the-loop fallback for high-stakes outputs. Hallucination rate measured and tracked weekly.
Yes — VPC-isolated deployments, Azure OpenAI or AWS Bedrock for data residency, on-prem fine-tuning, and no-data-leaves-perimeter contracts. We work to your data classification rules from day one.
Typical timelines: RAG copilot 6–10 weeks pilot to production, computer vision 8–14 weeks, predictive ML 8–16 weeks. Fixed-scope SOWs with weekly demos. No 18-month POCs that die on the shelf.
Two cost lines: inference cost (per-token API calls or hosted GPU compute) and platform cost (your infra + monitoring). We model both before pilot — typical enterprise copilot lands in $0.05–$0.50 per session at scale.
We start with prompting + RAG (cheap, fast, transparent). We fine-tune (LoRA / QLoRA on open-weight models) only when prompting hits a ceiling and the volume justifies the lift. Most engagements never need fine-tuning.
