Bring large language models into your business safely. We design, build, and integrate custom generative-AI solutions grounded in your own data — not generic tools bolted on the side.
Custom AI development means building generative-AI capability around your data, your workflows, and your security requirements — instead of forcing your business into an off-the-shelf tool. Apexify designs, builds, and integrates LLM solutions that answer from your knowledge, run where your compliance rules allow, and live inside the systems your teams already use.
Deliverables from a typical engagement:
Common builds include internal knowledge assistants for support and sales teams, drafting and summarization tools grounded in your templates and precedent, extraction services that turn unstructured inbound email into structured records, and customer-facing AI features embedded in existing products. The pattern is constant: your data, your guardrails, your systems.
We start with the business problem, not the model. LLM consulting engagements at Apexify follow a build-measure loop designed to kill weak ideas cheaply and ship strong ones fast.
Discovery. One to two weeks mapping use cases, data sources, security constraints, and success metrics. You get a ranked backlog with honest feasibility calls — including which ideas generative AI cannot do well yet, and which are cheaper to solve without it. Security and legal stakeholders join here, so approvals never stall the build later.
Prototype. A working proof of concept against real data, measured with an evaluation set you help define. Numbers, not demos, decide whether we proceed to a production build. Typical evaluation sets cover accuracy, groundedness, tone, and refusal behavior.
Production build. Hardening the prototype: retrieval quality, guardrails, latency, cost per query, monitoring, and fallback behavior. If your source data is not ready, our data engineering practice builds the pipelines first.
Handoff or run. We document the system, train your team, and either hand it over or keep operating it — your call. Evaluation suites transfer with the code, so quality stays measurable after we leave.
Generative AI consulting fails most often at the integration layer, and integration is where we live. As an official Salesforce Partner with deep CRM and data roots, we have spent years connecting systems and moving data cleanly — the unglamorous work that decides whether an LLM solution gets trusted or abandoned. We build assistants, generation features, and AI agents that sit inside real workflows, not in a separate tab nobody opens.
This service fits mid-market and enterprise teams with proprietary data — support archives, contracts, product documentation, CRM history — who need AI that respects security and compliance boundaries. If a configurable off-the-shelf product would serve you better, we say so in discovery and save you the build.
You also get a team fluent in the moving parts around the model — token costs, context limits, evaluation design, and a fast-shifting vendor landscape — so decisions about models and hosting are made on evidence from your workload, not on headlines.
Apexify is headquartered in Calgary and serves clients across Canada and the US remotely, with 20+ years of combined team experience and a 98% client satisfaction rate behind every build.
Cost scales with scope: a focused RAG assistant over a clean document set is a much smaller build than a multi-model platform with private deployment. Discovery is a fixed, low-cost phase that produces a scoped estimate, so you never commit to a build price before feasibility is proven on your own data.
Working prototypes typically land in 2-4 weeks; production systems in 8-16 weeks depending on integration and security requirements. The biggest variables are data readiness and how many systems the solution must connect to. We stage delivery so you see evaluated results early, before the larger build spend starts.
Yes. We deploy models in configurations that match your compliance needs — VPC-isolated cloud, private endpoints, or on-premises open-weight models — and design retrieval so sensitive data never leaves your boundary. Data-handling rules are set in discovery and enforced in the architecture itself, not left to a policy document.
No. We build, document, and can operate the solution for you. If you have engineers, we train them during the build and hand off with runbooks and evaluation suites. Many clients start with us running the system and take it in-house once it is stable and the team is comfortable.
Yes. Delivery is remote-first from Mountain Time, which overlaps every North American time zone, so working sessions are easy to schedule. US security reviews, contracting, and data-residency requirements are routine for us, and private deployment options cover clients with strict data-location rules.
Salesforce, CRM, and AI working as one system — explore the rest of the stack.