The questions
buyers actually ask.

A first production system is typically SGD 20k–80k as a fixed-scope build, with monthly operate-and-improve retainers from SGD 5k–20k depending on volume and eval complexity. Finyki prices most engagements as fixed-scope builds, so you approve the number before any work starts — no open-ended hourly meter. Larger platform engagements scale from there; we scope before we quote. See the full pricing breakdown for what moves the number.

Fixed price for defined builds — you approve the scope and the number up front. Iterative and ongoing work runs on a monthly retainer (typically SGD 5k–20k) instead of billable hours. We don't run an open-ended hourly meter because it's bad for both sides: it rewards slowness and punishes clarity.

We focus on builds substantial enough to pay for themselves — a first production system typically starts around SGD 20k. If your need is smaller than that, we'll say so and point you to a lighter option rather than take the work.

A first workflow or agent is usually live in 2–3 weeks, then handed to your team to own and extend. A full workflow platform of 10–20 orchestrated agents runs 10–16 weeks. We prefer to ship something real in the first few weeks and compound from there, rather than disappear into a six-month build.

Yes. For larger or uncertain scopes we start with a short, paid scoping sprint that maps the workflow, quantifies the hours it's eating, and produces a fixed quote for the full build. It de-risks everything before you commit, and it's credited toward the build if you proceed.

No. We build on your own infrastructure and hand over the prompts, evals, and code — you own it outright, with no per-seat SaaS lock-in to us. You still pay your own model/API and tooling bills directly.

You own everything — prompts, evals, deployment configs, fine-tune artifacts, logs. We ship into your cloud accounts (AWS, GCP, Azure), your data warehouse, your CRM. If you fire us tomorrow, your team keeps operating the work. We've written that into every master services agreement since 2023.

An AI automation agency designs, builds, and operates the agents, workflows, and data pipelines that replace high-volume manual work inside your company. For B2B operators, that usually means sales-research agents and support copilots, workflow orchestration across CRM and marketing systems, and RevOps intelligence layers that connect your warehouse to the team making calls.

A free 30–45 minute scoping call on your agenda, not a slide deck. You describe the workflow that's bleeding time; we tell you honestly whether AI is the right fix and roughly what shape the engagement would take. If it makes sense, you get a one-page written scope with the specific system, success metrics, eval plan, and a fixed price before anything is built. Start the conversation here.

Every build ships with an eval harness — a suite of tests defined against your definition of "good" that runs against real production data, usually from week 3. Most failed AI projects skip this: no eval harness, no retrieval strategy, no ownership of the prompt, so accuracy quietly slides to 40% and the team stops trusting the output. We shadow-run against the manual process and only go live once the outputs hold up. More on this in why B2B AI agents fail in production.

A SaaS tool gives you a generic agent that sort of fits your process. We build the system that fits your exact process, trained on your data, wired to your stack, with evals you define — and you own the output rather than renting it. Most of our clients have tried three SaaS tools first and come to us when the plateau hit.

In-house makes sense when AI is core product and you can hire and retain senior ML and platform engineers. For everything else, an agency ships the first production system in weeks instead of the months it takes to recruit a team — and hands it back documented so your people can extend it. The honest test: if you'd spend six months hiring before line one of code, start with an agency and bring it in-house once the pattern is proven.

Yes — and it's often better than a junior-staffed team billing at senior rates. Finyki is designed around a senior operator working with AI-assisted engineering, so the person who scoped your engagement is the person on the build calls. The leverage from modern agent tooling means a focused senior plus AI now ships what used to need a five-person pod, without the coordination tax or the handoff to someone eighteen months out of university. See how we think about it in the studio page.

Anthropic Claude and OpenAI for most agent and copilot work; open-source (Llama, Mistral) where sovereignty or cost dictates. Orchestration on n8n, Temporal, or direct code. Data stack on Snowflake / BigQuery, dbt, and Census / Hightouch. Tool choice follows the problem — we don't have a vendor we're paid to recommend.

Zapier is fastest for simple, linear SaaS-to-SaaS triggers but gets expensive and rigid at scale. Make handles branching visual logic more cheaply. n8n is our default for anything durable: it's open-source, self-hostable inside your own perimeter (which matters for PDPA and data sovereignty), and it doesn't meter you per task. For production B2B workflows we usually land on n8n or Temporal; Zapier and Make are fine for lightweight glue. We break the trade-offs down in detail in our n8n vs. Zapier vs. Make comparison.

Lead capture and routing, AI email and ticket triage, sales-research briefs before every call, RFP and tender monitoring, approval flows, RevOps scoring and forecasting, and internal knowledge copilots trained on your real documentation. A useful rule of thumb: any repetitive, rules-plus-judgment task your team does 20+ times a week is a candidate. Most B2B teams lose 20–30 hours a week to work no one should be doing by hand — see the workflow-automation ROI breakdown.

Both — that's the point. We're an AI automation and B2B marketing studio, so the same team that builds your agents also runs SEO, AEO, websites, and demand programs. That matters because a research agent that pre-briefs reps and an SEO program that fills the pipeline it works are the same growth system, not two vendors blaming each other.

SEO gets you ranked on Google; AEO (Answer Engine Optimization) gets your brand cited inside ChatGPT, Claude, Perplexity, and Google AI Overviews when buyers ask before they ever open a browser. They share infrastructure — clean content, schema, authority — but AEO optimizes the content shapes and citations that LLMs prefer to quote, and it's measured differently. We run both as one system; the deep dive is in AEO vs. SEO for B2B buyers and how to get cited by ChatGPT and Claude.

Honest, verifiable ranges rather than one hero number. Kacific (satellite internet, SG) saw 2.8× organic traffic and +41% enterprise inquiries; X-PHY (cybersecurity, US) saw 5.2× non-branded organic and +68% qualified demos; Flexxon (NAND, SG) grew keyword rankings +210%; Gauze Care (medical, US) saw +187% organic traffic and 3.4× e-commerce conversion. On the automation side, a licensed payments platform cut AML triage time by 71%, audit-traceable end-to-end. The full set lives in the case studies.

No. Finyki is a remote-first practice focused on Singapore B2B — SGT working hours, PDPA-literate, and grant-aware, without the overhead of a physical office. Nearly all AI automation work is delivered against your cloud and your repos, so proximity buys you nothing; responsiveness and stack fluency do. We work with teams the same way whether they're down the road or across an ocean.

Yes — about 40% of our work is with US-headquartered B2B companies, the rest across Singapore and Southeast Asia. The Singapore studio leads APAC engagements; our US coverage handles Eastern and Central time zones in the same working week.

Yes. Every engagement includes a PDPA review for customer data flows, data-residency configuration for embeddings and logs, and a purge-and-audit process for AI outputs that touch personal data. If your industry needs MAS TRM, HIPAA, or SOC 2 alignment, we scope for that up front — and self-hostable tooling like n8n keeps sensitive data inside your own perimeter.

Many of our Singapore engagements qualify for PSG, EDG, or the AI Accelerate programmes. We'll help structure the statement of work so it aligns with scheme criteria — we don't submit on your behalf, but we've written scopes for half a dozen approved grants. Background on the market thesis lives in our Singapore AI automation page.