AI Product Integration Services

AI product integration

AI features that actually ship.

Embedding Claude, GPT and open-weights into your product or website — drafting, summarisation, classification, copilots — with streaming UX, guardrails and real cost discipline. Not just an API call.

Streaming-first UX

Prompt-injection defended

Cost guardrails by default

Featured AI features

Scope an AI feature See AI features in production →

Docs›Q4 launch brief

Edited 2m

The Q4 launch will focus on enterprise readiness — SSO, audit logs, and SOC 2 alignment.

We expect three customer segments...

Continue writingRewriteShorter

Suggesting · streaming

We expect three customer segments to drive 80% of pipeline: mid-market security buyers, regulated FinTech CTOs, and existing enterprise accounts upgrading their plan

claude-sonnet · 280ms · $0.0008✓ cached

Cache hits

4.2× ↑

First token

280ms

60+

AI features shipped

280ms

Avg. first-token latency

4.2×

Prompt cache hit rate

99.9%

Feature uptime

The integration layers

Four layers. All four, on purpose.

The demo only needs the prompt. Production needs all four — and the gap between them is where most AI features quietly die.

Streaming UX

Fast first token, never a spinner.

Token-by-token streaming, optimistic UI, smart skeletons. The feature feels live from the first 200ms — not after a 4-second wait.

First-token <300ms target
Cancel & regenerate
Graceful degradation

Smart routing

Right model, every call.

Cheap models on simple work, frontier models on the hard parts. Automatic fallback to a second provider when the first throttles or fails.

Per-feature model policy
Provider fallbacks
Prompt + response cache

Guardrails

Holds up against weird input.

Prompt-injection defence, schema-validated outputs, refusal templates and an adversarial eval suite that runs on every prompt change.

Input sanitisation
JSON-schema validation
Adversarial eval set

Observability

Cost and quality, in plain sight.

Per-feature, per-user and per-model dashboards for tokens, latency and quality. Budgets with alerting so a runaway prompt doesn't blow the month.

Token + $ per feature
Quality regression alerts
Budget caps with paging

The integration stack

Frontier models, boring product engineering — exactly where each belongs.

Every tool below is in production on a live AI feature this quarter — no aspirational architecture diagram.

Anthropic Claude
Reasoning
OpenAI
Reasoning
Vercel AI SDK
Streaming
LangChain
Orchestration
TypeScript
Language
React
UI
Tailwind
Styling
Supabase
State + cache

Why most AI features die

The 10% that's easyand the 90% that isn't.

Anyone can call an LLM API. The hard part is what happens after the first prompt works in a notebook — streaming UX so the feature feels alive, guardrails so it doesn't leak or break, evals so changes don't silently regress, caching so the bill doesn't triple, and dashboards so you actually know what's happening.

Skip any of those and the feature dies one of three deaths: too slow, too unreliable, or too expensive to keep running. We build all five from day one — and we tell you honestly when a feature shouldn't ship at all.

2.8s

avg. latency on naive integrations

3.4×

bill blowout we see without caching

7 / 10

AI features killed within a quarter

Feature patterns

Six patterns that already earn their cost.

We'll only recommend a feature where the user value is clear, the UX shape is known, and there's a real metric to move.

01Drafting

In-product drafting & rewriting

Generate, rewrite, expand or shorten — inline in your editor, with selection-aware context and one-click accept.

Selection-aware prompts
Streaming token UI
Undo + diff view

02Summarise

Smart summarisation

Meeting recaps, long-doc condensing, daily digests — with adjustable length and styles your team can switch between.

TL;DR + bullets + briefs
Source-grounded recaps
Scheduled digests

03Classify

Classification & routing

Auto-tag, prioritise and route tickets, leads or content with schema-validated outputs and confidence scores.

Multi-label + hierarchical
Confidence-aware routing
Human review fallback

04Copilot

In-product copilots

Side-panel assistants that know what the user is doing in the app — and act on that context without context-switching.

Context-aware prompts
Action shortcuts
Conversation memory

05Extract

Extraction & enrichment

Pull structured data out of free text or documents — form-fill, lead enrichment, invoice line items — with strict JSON schemas.

Schema-locked outputs
Multi-modal sources
Confidence + audit log

06Search

Semantic & vector search

Replace keyword-only search with meaning-based ranking — across products, docs, tickets or your product catalogue.

Hybrid vector + keyword
Personalised re-ranking
Drop-in API

Features in production

Embedded, instrumented, still compounding.

Explore the full portfolio

SaaS · Productivity

AI rewriting in the editor doubled feature adoption.

2.1×weekly active feature use

B2B SaaS · CRM

Smart lead routing trimmed inbound triage to seconds.

−74%lead-to-owner time

Marketplace · Ops

In-product copilot cut user onboarding time in half.

−51%time to first "aha"

Our approach

Idea to live featurein six fixed stages.

Streaming UX, eval coverage and cost dashboards before launch — not in a retro after the feature has burned a hole in the bill.

Avg. build: 4–8 weeks

01
Feature discovery
We sit with product, look at the user journey, and pick the touchpoints where an AI feature would actually move a metric.
Deliverables: Feature map · KPI targets
Week 1
02
UX spec
Streaming flows, refusal copy, fallbacks, empty states — written before the prompt. Vibes are not a specification.
Deliverables: Feature spec · Figma
Week 1–2
03
Eval & guardrails
Golden set + adversarial prompts + schema validation, wired before the feature touches a user.
Deliverables: Eval suite · Guardrails
Week 2
04
Build & integrate
Plumbed into your codebase, tested against your design system, shipped behind a feature flag from day one.
Deliverables: PRs · Feature flag
Weeks 3–5
05
Cost & observability
Per-feature dashboards, budget caps with paging, and routing rules tuned against the real traffic mix.
Deliverables: Dashboards · Budgets
Week 6
06
Roll out & evolve
Staged rollout, quality regression alerts, quarterly retros on what the feature should learn next.
Deliverables: Rollout plan · Retro
Ongoing

Standard package

Streaming, evals, budgets

What's included

Every AI feature shipswith the system, not just the prompt.

Prompts in version control, evals in CI, cost dashboards in production — every box ticked before the feature flag flips.

01Streaming-first UX patterns built into your design system
02Version-controlled prompt library with diff review
03Eval suite (golden + adversarial) wired into CI
04Smart routing across providers with automatic fallbacks
05Prompt + response caching with measurable hit-rate
06Prompt-injection defence + schema-validated outputs
07Per-feature cost, latency and quality dashboards
0830-day post-launch tuning window — prompts, evals, routing

FAQ

Honest answersbefore you ask.

Can't find what you're looking for? Send a brief — we reply within a business day.

01: How is this different from just calling the API?
Calling the API is the easy 10%. Production AI features need streaming UX, fallbacks, eval suites, prompt-injection defence, schema-validated outputs, caching, routing across providers and per-feature cost dashboards. We build all of that — not just the prompt.
02: Which models do you use?
Whatever fits the feature. Anthropic Claude and OpenAI for most reasoning, open-weights for cost-sensitive work, smaller fine-tuned models where they outperform frontier ones. Smart routing chooses per-call with fallback to a second provider if the first throttles or fails.
03: How do you control costs?
Prompt caching, response caching, smaller models on simpler calls, per-feature budgets with paging when they trip, and dashboards that show $$ per feature, per user and per model. We catch runaway spend before invoices do.
04: How do you handle prompt injection?
Input sanitisation, system-prompt isolation, JSON-schema validation on every model output, and an adversarial eval suite that runs in CI on every prompt change. New attacks get added to the suite — once they pass once, they can't regress.

Let's scope

Got an AI feature that should already be live?

Book a feature-discovery call. We'll review the user journey, score the feature, and tell you honestly whether it's the right one to ship next — no decks, no pressure.