Production-grade AI workflow systems for SMEs. Not slide decks, not pilots that die — real systems, deployed and running, with the metrics to prove it.
Most AI consultancies build slides. We build systems. Here's what that means in practice.
Customer support, document retrieval, compliance checks — all done by hand. Your team is smart, but they're drowning in repetition.
SaaS tools want your data in their cloud. Singapore SMEs need PDPA compliance, control, and predictable costs — not per-seat SaaS tax.
Proof-of-concepts that die in demos. "Innovation projects" that never reach production. Expensive consultants who deliver a PDF and leave.
Self-improving customer intelligence engine — built in 48 hours, deployed, tested, running.
BOLDR Supply Co. is a Singapore watch micro-brand with a 3-person support team handling 70+ tickets per week across email, Instagram DM, WhatsApp, and web chat. Every enquiry was manual. The same questions recurred. Novel customer signals disappeared into inboxes instead of feeding back into product and marketing strategy.
BOLDR didn't need a chatbot. They needed a system that could:
The real insight from BOLDR's founder Leon: "The inbox isn't just people asking for help; it's people telling you exactly what matters to them." The system needed to close the loop between support signals and marketing action.
This isn't a demo. It's a reusable architecture pattern — the classify → retrieve → draft → approve → learn cycle applies to any product brand with a considered buyer and a small support team. We can re-skin this for specialty food, cosmetics, outdoor gear, or any Shopify-based niche brand in days, not weeks.
Total operating cost: SGD 22–57/month. No per-seat SaaS fees. No vendor lock-in. The client owns the system.
We don't build sequentially. A governed pod of 25 AI agents works in parallel — ingesting, analyzing, building, testing, and deploying simultaneously. This is how we ship production systems in days, not months.
Each agent has a defined role, governed by human checkpoints. The pod runs on OpenClaw with self-hosted LLMs — no data leaves your infrastructure.
Book a 30-minute strategy call. Tell us what you're trying to fix. We'll tell you whether AI can help — and if it can, we'll show you how.
Everything you need to know about working with Digital Futures.
Digital Futures Consultancy LLP (UEN: T17LL1937H) is a Singapore-based AI-native consultancy founded by Steve Ng — a CISSP, CCSP, and AWS Security Specialty certified engineer. We deliver production-grade AI workflow systems for SMEs through a governed 25-agent delivery pod. Unlike traditional consultancies that deliver slide decks, we ship working systems with tests, dashboards, and documentation — then train your team to run them.
We build across five capability areas:
Three reasons:
Measured ROI: 19–49× on our first deployed system (BOLDR customer intelligence engine).
We use Ollama Cloud because it provides access to frontier open models with the strongest data privacy guarantees in the industry:
This aligns with our data sovereignty commitment for Singapore SMEs subject to PDPA compliance.
Our core technology engine is the 25-Agent Delivery Pod — a governed multi-agent architecture running on OpenClaw with self-hosted LLMs:
Each agent has a defined role governed by human checkpoints. No data leaves your infrastructure.
Our self-hosted stack operates at SGD 22–57/month for LLM API calls only — with zero recurring SaaS fees. Project pricing depends on scope and complexity.
Book a strategy call to discuss your specific needs and get a scoped quote.
Yes — by architecture, not afterthought:
Our 25-agent delivery pod ships production systems in days, not months. The BOLDR customer intelligence engine — 9,200 LOC, 54 files, 31 API endpoints, 13/13 e2e tests — was built and deployed in 48 hours.
Singapore SMEs across retail, e-commerce, professional services, food & beverage, and technology. Our AI workflow patterns are sector-agnostic — the classify → retrieve → draft → approve → learn cycle applies to any business with repetitive processes and growing data.
Over 80% of AI projects fail to deliver business value, and 95% of generative AI pilots show no measurable ROI. The five root causes: failure to innovate (30–50% of time lost to compliance bottlenecks), failure to scale (risk concerns choke off production), no measurable ROI (vague objectives instead of specific metrics), the "science experiment trap" (building AI for the sake of AI rather than solving real business problems), and talent/data quality gaps. Read our full analysis →
AIRPF — the AI Innovation & Rapid Prototyping Framework — is our methodology for taking an AI project from idea to demo-ready in 21 days. It fuses Design Thinking empathy, Lean Startup speed, Agile structure, ML metric discipline, and evaluation focus into a single repeatable process. Every deliverable is measurable, every sprint has accountability, and every prototype maps directly to scoring criteria. Learn more about AIRPF →
AIRPF defines success metrics in Stage 1 (Discover & Define) — before any code is written. We measure accuracy (STT or RAG correctness), latency (response speed in milliseconds), and empathy (human-rated scores on a 1–5 scale). In Stage 4 (Validate & Measure), we run 5 tests per metric with diverse inputs. Every improvement is quantified. No project leaves our framework without data proving its value.