The questions everyone asks us

Honestly? It often isn't, a better process, a spreadsheet, or a simpler tool will do. Our free 30-minute consult ends with one of three answers: (1) yes, AI fits here and here's the shape of the project; (2) AI could help but there's a cheaper fix first; or (3) this isn't an AI problem. We'd rather lose a project than sell you something you don't need.

Small AI automations start around $3K–$6K per workflow. Mid-sized engagements (multiple automations or a single larger feature) typically land in the $15K–$60K range. AI FDE retainers are monthly per engineer, usually 50-70% of the cost of an equivalent full-time hire. We always quote a fixed fee or clear retainer before you sign anything, no 'day rate + we'll see.'

For scoped projects, there's no formal minimum, we take on small, high-ROI automations regularly. For embedded FDE, we ask for a 2-month minimum so the engineer has time to onboard and actually ship, but after that it's month-to-month.

Yes, and we recommend it. Most clients start with one workflow or one feature, something concrete enough to measure in 30-60 days. If it works, we expand. If it doesn't, you haven't committed to a year-long transformation.

Primarily remote with periodic on-site visits. We're headquartered in Singapore with engineers across Southeast Asia, and we regularly visit clients across the region. For embedded FDE engagements, we overlap at least 4 working hours a day with your team's timezone.

Senior engineers, minimum 5 years of shipped production experience, most with a decade or more. For FDE engagements, you meet and approve the specific engineer before they start. We do not route your project to anonymous offshore teams.

Code lives in your repository from day one. IP assignment is in every contract. If we stop working together, you keep 100% of what we built, fully documented.

For fixed-fee projects, we re-scope together, either as an amendment or as a follow-up sprint. For retainers, the scope is whatever you prioritize each sprint. Flexibility is the whole point of embedded engineering.

Yes, if configured properly. We default to enterprise/zero-retention APIs where your data is not used for training and is not retained. For highly sensitive data (PII, PHI, financial records) we can deploy open-source models on your own infrastructure so data never leaves your environment.

Yes. PDPA (Singapore) and GDPR (EU) compliance is built into how we handle client data, DPAs on request, encryption at rest and in transit, access logging, and data residency controls when needed. We can sign your security addenda and produce evidence for your compliance team.

Not yet. As a boutique consultancy we haven't pursued formal SOC 2 / ISO 27001 certification, these are typically more relevant at a larger product-company stage. What we do: follow SOC 2-style and ISO 27001-style control patterns (least-privilege access, audit logging, encryption, vendor review), work inside our clients' existing certified environments, and provide whatever documentation your auditors need. If your procurement requires a certified vendor, we'll be upfront about that.

Yes. For clients with hard data-residency or sovereignty requirements we run AI models (Llama, Qwen, Mistral) on your own GPUs, your own Kubernetes cluster, or your VPC. No data leaves your boundary.

Standard before the first technical conversation. We also sign DPAs, MSAs, and supplier documents. Our legal turnaround is usually 2-3 business days.

We're model-agnostic. Default: GPT-5 / Claude / Gemini via litellm for routing, with automatic fallbacks. For cost-sensitive or privacy-sensitive workflows we use open-source models (Llama 3, Qwen, Mistral) self-hosted via vLLM or llama.cpp. Model choice is a project-by-project tradeoff of accuracy, cost, and privacy, not dogma.

AWS, Google Cloud, Azure, plus hybrid with on-prem. We also deploy on Hostinger, DigitalOcean, Hetzner, and similar for smaller workloads where the big clouds are overkill.

n8n (self-hosted or cloud) is our default for most business process automation, it's visual enough for business users to read, but powerful enough for real engineering. We use Zapier / Make when appropriate, and drop to custom Python or TypeScript when the problem needs it.

Yes, where appropriate. Agents for multi-step reasoning with tool use, MCP servers to expose internal tools safely to LLMs. We're pragmatic, an agent is sometimes overkill when a well-prompted single call does the job.

Every production feature ships with an eval suite (LangSmith / DeepEval / Ragas / custom), tracing via Langfuse or Helicone, and CI checks that catch prompt or model regressions before they deploy. We treat prompts like code.

Singapore. We have a regional engineering network across Southeast Asia and primarily serve clients in Singapore, Malaysia, Indonesia, Philippines, Thailand, and Vietnam, with select work in the US and Europe.

We're a senior-heavy boutique team, not hundreds of junior developers. Our scale model is adding FDEs and specialists as clients need them, not hiring fresh graduates to look bigger.

English, Bahasa Indonesia, and Mandarin. Our AI deliverables can be localized into Bahasa, Tagalog, Thai, Vietnamese, and Mandarin, important for customer-facing AI in SE Asia.

Yes. Several clients use our FDE as a bridge while we help them interview, hire, and onboard permanent senior engineers. We can share our own hiring loop, coding rubrics, and reference interview questions.