AI Services
Production AI systems — RAG, agents, and LLM-backed features built into your existing product, with evaluation, observability, and a clear path to ROI.
Who this is for
SaaS founders, product leaders, and CTOs who need an AI feature shipped, not a slide deck.
What problem this solves
Most AI projects stall in the proof-of-concept stage because the team can demo a prompt but cannot ship a system. Costs are unpredictable, hallucinations are unmeasured, and the feature never makes it to a production SLA.
What you get
- A scoped AI feature shipped to production behind a feature flag in 4–10 weeks
- Evaluation harness with golden datasets, regression tests, and cost-per-request tracking
- Streaming UX, retry logic, and observable cost/latency dashboards
- Source code, documentation, and a runbook your team owns
How the engagement runs
- Day 1–3 — Scoping. We map the user job, draft an evaluation rubric, and quote a fixed scope.
- Week 1 — Prototype. A walking-skeleton end-to-end (data → model → UI) with the eval harness already in place.
- Weeks 2–6 — Build. Iterate on retrieval, prompts, tools, and UX against the eval set. Weekly demos.
- Week 7+ — Hardening. Cost guardrails, observability, A/B framework, security review, and a documented runbook.
Deliverables
- Production-ready code in your repo
- Evaluation harness + dataset
- Cost/latency observability dashboard
- 1-page runbook for your on-call
- 30-day handoff support window
Outcomes you can expect
- Predictable per-request cost (typically $0.001–$0.05 depending on model)
- Measurable accuracy on a frozen eval set, regressions caught at PR time
- Feature-flag rollout, no big-bang launches
Pricing & timeline
Most engagements run $8K–$45K USD as a fixed-scope build. Hourly available for embed/strategy work.
First production deploy in 4–10 weeks depending on scope.
Tech stack
- OpenAI, Anthropic Claude, Google Gemini
- LangChain, LlamaIndex, custom orchestration
- PostgreSQL + pgvector, Pinecone, Weaviate
- Vercel AI SDK, Supabase Edge Functions
- TypeScript, Python (FastAPI)
- Braintrust / Langfuse for evals & tracing
Relevant case studies
- AgentFlow - Visual AI Agent Builder — A no-code platform for building AI-powered automation agents through an intuitive drag-and-drop canvas interface.
- DaulatAI - AI-Powered Financial Advisor — An AI-powered investment advisor platform helping young Pakistanis achieve financial independence through personalized portfolio recommendations, Shariah-compliant options, and real-time market insights.
- AgenticAI - AI-Powered CV Screening Platform — An intelligent recruitment platform that uses AI to analyze and rank CVs against job requirements, helping companies find perfect candidates in minutes instead of weeks.
- Synthicare - NHS AI Clinical Decision Support — Synthicare is an AI-powered healthcare platform designed for NHS professionals, providing real-time diagnostic assistance, patient management, and clinical decision support integrated with NICE guidelines.
Frequently asked questions about AI services
- How is this different from hiring an AI consultancy?
- You get the engineer who codes it — not a partner who pitches and a junior who builds. Every commit is in your repo, and you can audit the eval set, the cost dashboard, and the runbook before you sign anything off.
- What if my use case needs a custom model, not just an API call?
- We've trained custom CNNs (94% accuracy on a 50-class image classification task), fine-tuned LLMs on private corpora, and built statistical models in R/Python. Custom training is a ~3-week add-on with its own scoping doc.
- How do you keep AI costs predictable?
- Three controls: (1) a per-request budget enforced in the SDK wrapper, (2) a cached retrieval layer so repeated queries are free, (3) a tiered routing layer (Haiku/4o-mini for cheap calls, Opus/4o for hard ones). You see the dashboard from day one.
- Do you sign an NDA?
- Yes — mutual NDA before the first scoping call. Standard MSA + SOW for the engagement.
- Who owns the IP?
- You do. The contract assigns all custom work to you on payment. We retain rights only to the underlying frameworks (open-source) and our own internal tooling.
- Can you work inside our existing repo and CI?
- Yes. We follow your branch protection, code review, and deploy process. We don't push to main without review.