AI inside your product
Smart search, summaries, drafting or recommendations added to your existing app — a feature your users feel, not a separate tool.
AI, added to what
you already run.
You don’t need to rebuild your product to make it intelligent. We add AI to the tools, workflows and data you already have — connected securely, wrapped in guardrails and ready for daily use in weeks.
No rebuildworks with your
existing stack
existing stack
Your datastays private and
under your control
under your control
Weeksto a working
integration, not quarters
integration, not quarters
OpenAI & Claude APIs
Private / self-hosted models
Fits your existing stack
Guardrailed & observable
Common starting points
Where an integration
Where an integration
earns its keep.
Most teams don’t need a new product — they need intelligence in the one they already run. These are the places it pays off fastest.
Automate a workflow
Point AI at a repetitive internal process — triage, classification, data entry — and hand your team back the hours.
Unlock your data
Make scattered documents and records answerable in plain language, grounded in your own sources with citations.
Connect your tools
Let AI read and act across the software you already use — CRM, helpdesk, spreadsheets — inside the permissions you set.
What’s included
Everything the
Everything the
integration needs.
A complete, production-minded integration — not a demo script. We handle the model, the plumbing and the safety around it.
01
The right model, wired in
LLM API integration
OpenAI, Claude or an open model connected to your backend with retries, streaming, cost controls and sensible fallbacks.
OpenAIClaudeStreamingCost controls
02
Users feel it, not find it
In-product AI features
AI surfaced right where the work happens — drafting, search, summaries — designed into your interface, not bolted beside it.
Smart searchSummariesDraftingRecommendations
03
Read and act in your stack
Tool & API hooks
Secure connectors to your CRM, helpdesk, database or internal APIs so the model can fetch context and take scoped actions.
CRM & helpdeskInternal APIsWebhooksScoped actions
04
Grounded in your reality
Data & knowledge access
A retrieval layer over your documents and records so answers cite your own sources instead of guessing from training data.
RAGVector searchCitationsPermissions
05
When data can’t leave
Private & self-hosted models
Open models deployed in your own cloud or on-prem for the cases where privacy, latency or cost rule out a public API.
Open modelsYour cloudOn-premVPC
06
Safe, measured, watchable
Guardrails & orchestration
Prompt orchestration, input/output guardrails and tracing so the feature behaves, and you can see exactly what it did.
GuardrailsOrchestrationEvalsTracing
How we work
From your stack
From your stack
to a live feature.
A short, low-risk path that respects the system you already have — measured before it ships and observable after.
Fit assessment
We look at your stack and the use case, confirm AI is the right tool and agree what a successful integration looks like — and how we’ll measure it.
Secure data access
We connect only what’s needed, with least-privilege keys and clear boundaries, so the model sees your data without your data leaving your control.
Integration build
We wire the model into your product or workflow with retrieval, tools and orchestration — the part that turns a prompt into a real feature.
Guardrails & evals
We add input and output guardrails and an evaluation set, so quality is a number we watch and unsafe or off-topic responses are caught.
Testing
We test against real inputs and edge cases, tune retrieval and prompts, and confirm the feature holds up before a single user touches it.
Rollout & monitoring
We ship behind a flag, watch cost, latency and quality in production, and iterate on evidence — not on a hunch.
Direct answers
Questions before
Questions before
you plug in.
A focused technical conversation is the fastest way to know if an integration is worth it — no pitch deck required.
Ask us somethingNo. That’s the whole point of an integration — we add AI to your existing stack through APIs, connectors and a retrieval layer, without rewriting what already works.
No. We use providers and settings that don’t train on your data, connect with least-privilege access, and deploy privately or on-prem when the data demands it.
We’re model-agnostic — OpenAI, Claude and open models — and choose per use case for quality, latency, privacy and cost, keeping the door open to switch later.
A focused integration is commonly a few weeks. The timeline depends on how many systems we connect and how clean the data is — which the fit assessment makes clear up front.
It depends on scope and integrations. After a short discovery we give a range tied to milestones, plus a clear view of ongoing model and infrastructure costs so there are no surprises.
Contact
Let’s make the next
Let’s make the next
move count.
Tell us what you are building. We will come back within one business day with questions, not a pitch deck.