How do you integrate AI into existing software and workflows?
We map the integration points first: the API contract your app calls, the data the model reads and writes, the auth and permissions it inherits, and the events it reacts to. Then we serve the model behind a gateway, wire it to those systems with tool and function calling for agents, and add the evals, observability and rollback that keep it reliable. For the engineering checklist behind a production rollout, see our AI deployment checklist.
How much does AI integration cost?
It depends on system complexity and stage, so we fix scope and price only after a short assessment. Most engagements start with a fixed-price deployment readiness review, then move into a recovery or greenfield build scoped from there. We deliver onshore-quality engineering at a fraction of typical onshore cost, and the five locked numbers, including a cost-per-call ceiling, keep ongoing run cost predictable.
How do you choose an AI integration partner?
Look for a partner who commits to production numbers in writing, ships evals, observability and rollback rather than just a demo, and hands the system back so your team can own it. Ask how they handle a provider model change, a cost spike and a bad release. Resourcifi has shipped software since 2017, holds a 4.9 rating on Clutch, and works with an in-house team of 200+ experts, so there is no subcontracting between you and the engineers doing the build.
What does production recovery actually involve?
You hand us an AI system that is failing one or more of the five constraint numbers, such as latency above spec or model cost over budget. We instrument it, write a deployment readiness report, propose a remediation plan, then fix the failure modes and add the evals, observability and rollback paths it was missing.
What is the five-number deployment constraint set?
It is five numbers we lock with the client before writing deployment code: p95 latency target, cost-per-call ceiling, throughput floor, accuracy floor on the reference dataset, and recovery time objective. They define what production means for your system and become the thresholds the release pipeline enforces automatically.
Which clouds and models do you deploy to?
We deploy to whichever cloud your data already lives on, including AWS, Google Cloud and Azure. We are model-agnostic: frontier models from OpenAI, Anthropic and Google, plus open-weight models such as Llama or Mistral for on-prem and cost-sensitive workloads, routed through a gateway so models can be swapped without code changes.
What is in the hand-off pack?
Architecture diagrams, runbooks for the most likely incidents, a prompt registry with rollback procedure, an eval dashboard walkthrough, a model upgrade SOP, a cost dashboard, a security checklist, and two weeks of paired on-call. It is designed so your team can own and maintain the system without us.
How do you keep LLM costs under control after deployment?
Cost is one of the five locked constraint numbers, so it is enforced at release time. We use a gateway for model routing, response caching, prompt redesign and per-request cost caps. On inherited systems, routing simpler queries to smaller models and adding caching often reduces spend significantly while holding accuracy.
What does your eval suite cover?
Three layers: a reference dataset of representative queries that sets the accuracy floor, an adversarial set for known failure modes such as prompt injection and out-of-domain hallucination, and a regression set where every past production incident becomes a permanent test. Evals run pre-deploy on every change, post-deploy on a schedule, and against live traffic samples.
How do rollbacks work if a release goes wrong?
Every deployment supports three independent rollback paths: model-version rollback by pinning the registry with no rebuild, prompt-version rollback in LangSmith or Langfuse, and feature-flag traffic routing. Releases ramp as a canary through 1%, 10%, 50% and 100% of traffic, with automated rollback the moment any constraint number breaches its threshold.
Do you offer ongoing SLAs after the hand-off?
Yes, optionally. A standard tier covers 99.5% uptime, a p95 latency target and a 24-hour response on production-blocking incidents. An enterprise tier covers 99.9% uptime with on-call paging, a defined RTO and RPO per system, and quarterly disaster-recovery testing. Every SLA is tied to live dashboards the client can see.
How is integration different from your AI consulting service?
AI consulting is advisory: assessment, roadmap and build-versus-buy decisions. AI integration is the engineering that wires a model into your systems and turns a pilot into a production system serving real users, with evals, observability, rollback and a maintainable hand-off. Many clients start with an assessment and move into integration once the constraint set and plan are agreed.
