Scope this with us.
Send a 2-line description and your budget. We come back with a written scope and a realistic delivery window — within 4 business hours.
Services · AI
AI integration for your product.
Your product needs AI the right way — not a GPT wrapper, not a demo, but a real, evaluated, cost-controlled feature that users want to pay for. We design, build, and measure AI features for SaaS and web apps, with a strong bias toward what actually works vs. what demos well.
Section 01What we deliver
Scope and typical turnaround.
No rigid price lists. Scope is shaped around your budget and requirement — the turnaround below is a realistic expectation for each deliverable, not a quote.
#DeliverableWhat's includedTurnaround
01
AI feature scope + spike
2-week paid spike. We build a functional prototype + cost model + eval harness. You decide whether to proceed.2 weeks02
RAG / semantic search
Document ingestion, vector DB, hybrid retrieval, reranking, citations. Leading language models.3–5 weeks03
AI copilot / chat
In-product chat with structured tool calls, streaming UI, conversation memory, guardrails.4–6 weeks04
Document parsing / OCR pipelines
Turn PDFs and images into structured data. Invoice extraction, resume parsing, contract review.3–5 weeks05
Agentic workflows
Multi-step agents that plan, call tools, and return verified results. Observability included.6–10 weeks06
AI cost audit + optimisation
We look at your existing OpenAI / Anthropic spend and cut it 30–60% without quality loss.2–3 weeksSection 02Stack
What we build with.
We use widely-adopted, maintainable tooling. Your team (or the next engineer) will recognise everything.
·LangChain
·LlamaIndex
·pgvector
·Weaviate
·Pinecone
·Langfuse
·Braintrust
·Replicate
Timeline
Spike: 2 weeks. First production feature: 3–6 weeks after spike sign-off.
Section 03FAQ
Common questions, honest answers.
Q/01 Are you just prompt engineers?+
No. We build full AI systems: retrieval, reranking, evaluation, guardrails, cost control, observability. Prompts are 10% of the work. The other 90% is the surrounding engineering that makes the feature actually work for your users.
Q/02 Which model do you recommend?+
It depends on your use case — long-context reasoning, speed, complex tasks, or on-premises deployment each call for different architectures. We pick based on your latency, cost, and quality targets — not based on hype.
Q/03 How do you measure AI quality?+
Every engagement ships with an eval harness. We define success metrics (accuracy, latency, cost per request) before building, and track them continuously. No subjective 'it seems fine' sign-offs.
Q/04 What about hallucinations?+
Mitigated by retrieval (so the model answers from your data, not its memory), structured outputs (so the model cannot invent free-form facts), and eval gates (so regressions get caught). We never ship a 'please do not hallucinate' prompt and call it done.
Q/05 On-prem or cloud?+
Both. For regulated industries (healthcare, finance, government) we deploy open-source models on your own AWS / Azure / GCP infrastructure. For everyone else, managed AI APIs are usually faster and cheaper.
Q/06 Can you help us get an AI feature to market quickly?+
Yes — that is literally the 2-week spike. By the end of it you have a functional prototype and a recommendation on whether the feature is worth building. Many clients use the spike as a decision point before committing larger budget.
Section 04Related
Other services we run.
WEB
Web development
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MVP
Product and MVP
You have an idea that investors or early users need to see. We translate it into a shipping, revenue-capable p…
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TEAM
Dedicated engineer
You need an engineer working full-time on your product, but the freelancer market is a lottery and a full loca…
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