AI INTEGRATION

AI Integration

Add AI to your existing product without rebuilding it.

Embed LLMs, RAG, and intelligent agents into the apps you already run.

support-bot.pinnacle.codes
LIVE
User
How many orders did we ship yesterday?
Assistant
queried orders.db
Yesterday you shipped 847 orders (+12% w/w). The top SKU was the Mid-Layer Hoodie with 64 units.
responded in 1.2s · frontier LLM · 412 tokens
Assistant is typing…
Ask anything about your business…⌘K
The pipeline

From prompt to production answer in milliseconds.

INPUT
User query
GATEWAY
Your API
CONTEXT
Vector DB
INFERENCE
LLM
OUTPUT
Response
< 2s
Avg latency
99.9%
Uptime SLA
3 LLMs
Fallback chain
100%
Audit logged
What we build

Capabilities, not chatbots.

Capability 01

Retrieval-augmented generation

Ground LLM responses in your own knowledge base. We build the ingestion, chunking, embedding, and retrieval pipeline so answers are accurate, citable, and current.

action.execute
query_db("orders", last="7d")
send_email(to="ops@", body=...)
create_calendar_event(...)
Capability 02

Agents and function calling

Models that take action: query databases, fill forms, send emails, call your APIs. Built with tool-use, careful permissions, and observable execution.

What’s our churn rate this month?
3.2%, down from 4.1% last month.
typing…
Capability 03

Chat assistants

In-product copilots, customer-facing support bots, and internal helpdesks that actually know about your business and stay in your brand voice.

PASS
PII
PASS
Toxicity
PASS
Jailbreak
Capability 04

Safety and evals

Output filters, jailbreak resistance, PII scrubbing, and offline evals that run on every prompt change so quality never regresses.

Cost / 1k req−68%
JanFebMarAprMayJun
Capability 05

Cost and latency tuned

Streaming responses, caching, smart model routing, and prompt optimization. We track per-request cost so your AI bill stays predictable.

Slack
Salesforce
HubSpot
Notion
Linear
Stripe
Capability 06

Plugs into what you have

Salesforce, HubSpot, Notion, Slack, your custom backend. We integrate where your team already works rather than building yet another tool.

How we ship it

Four phases. Six to twelve weeks.

01

Use case discovery

Find the workflows where AI moves the needle on revenue, retention, or cost. Skip the rest.

02

Prototype in 2 weeks

Working demo on real data within a sprint. Validate quality and economics before committing to a build.

03

Production hardening

Auth, logging, evals, fallbacks, rate limits, monitoring. The boring stuff that decides whether your AI ships or stays a demo.

04

Continuous improvement

We instrument every output, run weekly evals, and tune prompts/models against real usage. AI products get better; static ones decay.

Questions

Frequently asked.

Anything else? Ask us directly.

frontier and open-source models?

Depends on your task, data sensitivity, and budget. We benchmark options against your actual workflow and recommend based on quality, cost, and latency. Frontier models for hard reasoning; smaller fine-tuned models for high-volume classification.

Will my data train someone else's model?

No. We use zero-retention API tiers from major providers, or self-hosted models on your own infrastructure for highly sensitive data. Contracts and DPAs cover the rest.

How do we measure if the AI is good?

Offline evals on labeled examples, plus production telemetry: thumbs-up/down, regeneration rate, downstream conversion. We build the eval harness as part of every engagement.

What does it cost to run?

Inference cost varies wildly by use case. A customer-support chatbot might run $200/month at low volume; a document-processing pipeline might run $5,000. We model this upfront so there are no surprises.

Let’s talk

Start your AI Integration project.

Send us a short brief. We reply within one business day with a recommended next step, an honest range, and the name of the person who would lead the work.