What you get
- A production-ready AI feature that users adopt from week one
- Latency and cost controls built into the architecture, not bolted on later
- Evaluation benchmarks for quality, safety, and regression tracking
Build
AI product engineering
Your competitors shipped their AI feature last quarter. You're still in planning.
40+
AI features shipped
12
Weeks to launch
97%
Model accuracy targets met
Trusted by teams at
The Problem
What problem does this service solve?
You have a roadmap and a market window. Your team has never shipped AI to production and every sprint gets stuck on the same four questions.
Every quarter without a shipped AI feature is a quarter your competitors are building switching costs into their product. After four quarters, the gap stops being technical and starts being commercial.
Overview
What is AI product engineering?
Most teams can get an AI demo running in days. Shipping one that users trust, that scales, and that your team can run after launch - that's a different problem entirely.
Most teams can get an AI demo running in days. Almost none can ship one users actually trust.
We treat AI features as product systems, not experiments. That means quality thresholds, fallback paths, telemetry, and UX patterns that set real expectations up front.
The result is a shipped capability your team can run and improve after launch, not a fragile feature that breaks the first time a real user touches it.
Experience Signal
100+ products shipped across growth-stage and enterprise teams. 12 weeks is our default, not our stretch goal.
What we build
AI product engineering services we deliver
AI copilots inside your product
In-product assistants that read your data, take permission-aware actions, and leave an audit trail. Users finish complex tasks without leaving the app.
RAG search and Q&A over your docs
Retrieval-grounded answer systems with citations, confidence thresholds, and clean fallback to human support when the model isn't sure.
AI agents for back-office work
Autonomous workflows that handle ticket triage, lead qualification, document review, and other repeat tasks your ops team loses hours to every week.
Voice AI for phone and support
Outbound and inbound voice agents powered by ElevenLabs and Whisper. Zero-install for the caller, sentiment and keyword tracking for you.
Document intelligence and OCR
AI-powered invoice, contract, and form processing. Pulls structured data out of scans and photos and writes it straight into your systems.
Predictive scoring and classification
Lead scoring, churn prediction, support routing, and other signals your team currently guesses at. Evaluated, tuned, and instrumented before launch.
Multi-model orchestration and fallback
Route requests across GPT-5, Claude, Gemini, and open-weight models based on cost, latency, and accuracy. No single-vendor lock-in.
Evaluation, guardrails, and observability
Test harnesses, quality rubrics, and live dashboards so you catch drift the day it happens, not the day a user complains on Twitter.
Fit
Is this service right for you?
Good fit
- You're the VP of Product who's been told to add AI to the roadmap, but your engineers have never shipped an ML feature
- You have a working demo that impressed the board, but nobody trusts it enough for production users
- Your team can build features fast, but AI architecture decisions keep stalling the sprint
- You're modernizing a profitable product with AI and can't afford a six-month science project
- You have the data and the users - you just need senior AI engineering to ship
Not the right fit
- Teams looking for one-off prompt experiments with no launch plan
- Projects without a clear owner for post-launch iteration
- Use cases where no real product workflow exists yet
Process
How does AI product engineering delivery work?
1
Phase 1· Week 1-2
Use-case framing and technical scoping
We map business outcomes to specific AI behaviors and set acceptance criteria before a single architecture decision gets made.
Deliverables
- Feature scope with measurable success criteria
- Model and retrieval strategy options with tradeoffs
- Risk map covering accuracy, latency, and cost
2
Phase 2· Week 2-4
Architecture and evaluation design
We design the orchestration flow, data boundaries, and quality evaluation so engineering and product share one definition of done.
Deliverables
- System architecture and orchestration flow
- Evaluation dataset and test scenarios
- Fallback and guardrail strategy
3
Phase 3· Week 4-10
Build, integrate, and iterate
We ship the feature inside your product stack, instrument quality, and iterate against real usage signals from week one.
Deliverables
- Production-grade feature implementation
- Telemetry for quality and cost
- Admin controls for prompts, thresholds, and rollouts
4
Phase 4· Week 10-12
Launch hardening and handover
We finish performance hardening, rollout strategy, and internal training so your team can run the feature confidently after release.
Deliverables
- Launch checklist and rollout plan
- Operational runbook and incident playbook
- Post-launch optimization backlog
Outcomes
- A production-ready AI feature that users adopt from week one
- Latency and cost controls built into the architecture, not bolted on later
- Evaluation benchmarks for quality, safety, and regression tracking
- A team that knows how to ship the next AI feature without us
Deliverables
- Use-case and model strategy aligned to business goals
- Prompt, retrieval, and orchestration architecture
- Evaluation suite with test scenarios and acceptance thresholds
- Launch-ready feature with monitoring, fallback paths, and docs
- Post-launch iteration plan tied to product metrics
Success Metrics
- AI feature activation and repeat-usage rate in the first 30 days
- Response latency at p95 and p99
- Cost per successful AI interaction
- Quality score against your defined evaluation rubric
- Incident rate and mean time to detect model regressions
Engagement models
12-week end-to-end delivery for one critical AI feature, from scoping to launch hardening.
Best forA focused launch with one high-priority feature and a fixed delivery window.
AI models we work with
GPT-5
OpenAI
General reasoning, tool use, and the default fallback when latency isn't critical.
Claude Opus 4.6
Anthropic
Complex agents, long-context work, and anything that needs careful reasoning over your data.
Claude Sonnet 4.6
Anthropic
Production workloads that need smart outputs at a price that won't blow up your margins.
Gemini 2.5 Pro
Multi-modal features and cases where native image or video understanding earns its keep.
Llama 3.3
Meta
Self-hosted workloads, regulated data, and anywhere sending text to a third-party API is a non-starter.
Whisper + ElevenLabs
OpenAI / ElevenLabs
Voice AI - speech to text in, natural-sounding voice out, live on customer calls.
Use Cases
Common use cases for AI product engineering
In-product AI copilot for SaaS
Users get stuck on complex workflows and churn before they reach the "aha" moment.
How we build it
We build a contextual assistant grounded in product data with permission-aware actions and full audit trails.
Outcome
Users finish the workflows they used to abandon, and high-value features stop sitting unused.
Knowledge assistant for support teams
Support agents spend hours searching docs and pasting the same answers to the same questions.
How we build it
We implement retrieval-based answer generation with citations, confidence thresholds, and clean handoff to humans when the model isn't sure.
Outcome
Agents handle more tickets per shift without the quality drop that usually comes with scale.
AI interview and qualification flows
Sales or talent teams need consistent qualification at volumes their humans can't hit.
How we build it
We design voice or chat-based structured interview flows with scoring, CRM sync, and full call transcripts.
Outcome
Four times more qualified conversations per week with the same headcount and zero drop in rubric consistency.
What clients say
Real feedback from real teams
I spent years at Amazon fighting static surveys. RaftLabs built a working prototype in four days that already outperformed every survey tool I'd used. Twelve weeks later we had a full SaaS that product teams actually want to use.
Founder
Ex-Amazon PM - Perceptional
We went from text surveys that nobody finished to AI phone interviews that people actually enjoy. The voice agents handle the whole conversation, and the analytics tell us what we need to know without reading a single transcript.
Cherian Koshy
Behavioral Strategist - USA Today Bestselling Author
Proof
Recent ai product engineering work
BuildConversational AI survey SaaS for Perceptional
4x
Deeper insights
12 weeks
Concept to launch
“Working prototype in four days. Full SaaS in twelve weeks.”
Read case studyBuildVoice AI interview platform for Cherian Koshy
12 weeks
To production
Global
Call reach
“Text surveys nobody finished became phone interviews people enjoy.”
Read case studyIndustries
AI product engineering for your industry
Fintech
Fraud scoring, KYC automation, and agent-driven onboarding for teams that can't afford a shaky launch.
ExploreHealthcare
Patient intake, clinical summarization, and RAG over your protocols - with the guardrails regulated data demands.
ExploreMarTech
Conversational surveys, voice interviews, and the kind of insight work that used to need an analyst team.
ExploreRetail
Inventory OCR, price intelligence, and copilots that let store staff move faster than the spreadsheets they replaced.
ExploreHospitality
Booking copilots, guest-facing agents, and voice AI for front desks that don't have staff to spare.
ExploreInsurance
Claims triage, document extraction, and underwriting assistants that cut handle time without cutting corners.
ExploreFrequently asked questions about AI product engineering
Most focused launches fit a 12-week window. Timeline depends on data readiness, workflow complexity, and how deep the feature has to reach into your existing product.
Related Services
Next Step
Ready to ship your first AI feature?
Tell us what you're building. We'll show you the fastest path to production and flag the risks before they cost you a quarter.