Most enterprise AI projects stall between demo day and deployment. We build the data foundations, guardrails, and operations that turn pilots into production systems.
· We don't train foundation models. We deploy them well.
· Most enterprise problems don't need a fine-tune — they need better retrieval, evals, and ops.
· Data quality beats model choice, every time.
· An AI feature without monitoring is a liability, not an asset.
Ready to ship internal tools that actually get used, not just shown.
Years of documents, tickets, and transactions — but nothing that surfaces patterns.
Pilots completed, results unclear, no path to production, no one accountable.
Worried about IP exposure, PDPA, or sending customer data to model providers.
Data Foundations
AI Applications
AI Operations · Most teams skip this. We don't.
Most enterprise AI projects fall into a handful of shapes. Here's what we've built repeatedly.
Search across SharePoint, Confluence, Notion, Slack, and tickets — with citations, access control, and an audit log of every query.
Extract structured data from contracts, invoices, KYC documents, and claims. Human review for edge cases. Full audit trail.
Agent-assist summaries, draft replies, and ticket classification. Humans stay in the loop on every customer-facing output.
KYC checks, AML screening against sanctions lists, regulatory text monitoring for policy changes. Built with auditability from day one.
We design for deployment from week one — model serving, monitoring, cost controls, fallback strategies. If it can't reach production, we won't start.
VPC deployments, private endpoints, zero-retention API agreements, on-prem options for sensitive data. PDPA-friendly by design.
Claude, GPT, Gemini, Llama, Mistral — picked by fit, not by partnership pressure. We benchmark on your data, not on public leaderboards.
AI projects fail most often at integration boundaries — model → vector DB → API → auth layer. We own all of it. No three-way vendor blame games.
Models Claude · GPT · Gemini · Llama · Mistral Frameworks LangChain · LlamaIndex · custom orchestration Vector DBs pgvector · Pinecone · Weaviate · Qdrant Evaluation Ragas · Promptfoo · custom eval suites Observability Langfuse · Arize · Datadog Platforms AWS Bedrock · Azure OpenAI · Vertex AI
We pick boring tools by default. We pick exciting ones when there's a real reason — and we tell you why.
⭐ Most clients start here
Use case prioritization, feasibility, ROI model, working POC, path to production.
Duration: 2–3 weeks
Pricing: Fixed fee
Build
One use case from zero to production, with evals, monitoring, and handover.
Duration: 8–16 weeks
Pricing: Milestone-based
Operate
Continuous monitoring, eval regression, prompt iteration, model upgrades, cost optimization.
Duration: Ongoing
Pricing: Monthly retainer
Every system we ship includes:
We treat AI risk the way we treat security risk — designed in, not bolted on.
Legal · RAG · pgvector · Bedrock · 10 weeks
Read full caseNo — we deploy AI in configurations specifically designed to prevent this. Enterprise API tiers (Anthropic, OpenAI Enterprise, Azure OpenAI, AWS Bedrock) all offer zero-retention or zero-training agreements. For higher-sensitivity workloads, we deploy open-source models in your VPC.
We benchmark per use case. Claude tends to lead on long-document reasoning and code. GPT tends to lead on broad reasoning and function calling. Gemini has strong native multimodal handling. Llama and Mistral are our defaults for data-sensitive deployments.
We build for this assumption. Every system includes eval suites, confidence scoring, human review pipelines for high-stakes decisions, and output-drift monitoring. We treat hallucination as an engineering problem, not "a feature of LLMs."
Discovery Sprints are typically SGD 25K–45K. Production builds range SGD 80K–250K depending on scope, integrations, and complexity. Managed AI Operations start at SGD 10K/month.
Documentation and handover are part of the engagement, not afterthoughts. We deliver architecture diagrams, prompt libraries with rationale, runnable eval suites, runbooks for model upgrades, and at minimum two knowledge-transfer sessions.
Sometimes — but rarely as a first step. Most enterprise problems don't need fine-tuning; they need better retrieval, evaluation, and prompts. When we do fine-tune, we use LoRA / PEFT methods that are cheap to maintain.
Let's run a Discovery Sprint. Two weeks, fixed fee, real output — not a sales pitch.
Book a 30-min AI Chat