Services · 02
Generative AI integration for products and internal tools
We embed generative AI into existing products with retrieval grounded in your data, guardrails, evaluation, and privacy controls.
Generative AI belongs inside the workflows your users already have—not as a standalone chat window bolted onto the side. That means assistants embedded in dashboards, summarization on ticket threads, search over internal knowledge, or drafting tools that respect roles and data boundaries. The integration work is as much about auth, retrieval, and failure behavior as it is about choosing a model.
Most product features that need answers grounded in company data start with retrieval-augmented generation (RAG) over approved sources: documents, tickets, product specs, or policy libraries. We design ingestion pipelines, chunking strategies, and access controls so users only retrieve what they are allowed to see. When retrieval finds nothing relevant, the feature should refuse or escalate clearly instead of inventing an answer.
Quality is an engineering concern, not a one-time prompt tweak. We add evaluation harnesses for critical prompts, logging for failures, and versioning so changes can be tested before release. Guardrails cover PII handling, off-topic requests, and content policies appropriate to your domain—especially important in healthcare, education, and regulated contexts.
Privacy and hosting choices follow your requirements. That includes retention rules, provider selection, and whether sensitive data stays in your VPC or approved regions. NDAs are available before you share proprietary material. We document data flows so your security review has concrete artifacts, not hand-waving about "the cloud."
We usually start with one high-value flow inside your current product—an in-app copilot, a summarization panel, or grounded search—then expand once retrieval, auth, and monitoring are solid. That phased approach reduces risk and gives you real usage signal before investing in broader AI surface area.
Problem we solve
Adding AI to a product is easy to demo and hard to run safely with private data and consistent quality.
Ideal for
Product teams adding AI to SaaS or internal tools without risking customer data.
Use cases
Concrete examples of how teams use this service.
In-product copilot for complex workflows
Users get contextual suggestions, form prefill, and step guidance inside your app—grounded in product state and help docs, not generic chat advice.
RAG search over internal knowledge
Staff or customers search policies, specs, and support history with citations to source material and clear behavior when nothing relevant is found.
Summarization for tickets and threads
Long email chains, support conversations, or meeting notes collapse into structured summaries with key actions flagged for human review.
Content drafting with approval
Marketing, support, or ops teams draft emails and articles from templates and brand guidelines, with edits and publish steps staying under human control.
Grounded Q&A for customer-facing products
Shoppers or learners ask questions answered only from approved catalog, curriculum, or policy data—with refusal behavior when the corpus has no match.
Evaluation and prompt lifecycle
Critical prompts ship with test sets, regression checks, and version history so quality does not drift silently after the first launch.
Deliverables
- Production AI feature with access controls
- Data ingestion and retrieval pipeline
- Privacy and retention guidance for your stack
- Quality evaluation baseline
What we build
- Chat assistants and in-product copilots
- RAG over documents, tickets, and knowledge bases
- Summarization, search, and content workflows
- Evaluation harnesses, prompt versioning, and guardrails
Tech stack
OpenAI and other LLM APIs, vector databases, Next.js, Node.js, PostgreSQL
Industries we serve
How this service shows up in the industries we work with. See all industries.
What affects cost and timeline
- Scope of the AI feature—single summarization endpoint vs. multi-source RAG with role-based retrieval.
- Data preparation: clean corpora, access rules, and ingestion pipelines take time when sources are scattered or unstructured.
- Evaluation and guardrail depth for domains where wrong answers carry reputational or compliance risk.
- Integration with existing auth, billing, and UI patterns in your current application.
- Ongoing model usage is usage-based; architecture choices (caching, retrieval filters) affect run cost at scale.
For a directional band, use the project cost estimator, then we refine on a call.
Why work with Apexic
- We treat retrieval, auth, and refusal behavior as first-class requirements—not optional polish after the demo works.
- Evaluation harnesses and prompt versioning ship with critical features so quality regressions are caught before users see them.
- Privacy and data-flow documentation are part of delivery, making security review straightforward instead of a late surprise.
- We embed AI into existing product flows rather than shipping disconnected chat widgets that users abandon.
How engagements run
Discover, design, build, launch, and support, with clear checkpoints at every stage.
Discover
We align on outcomes, users, budget band, and technical constraints.
Design
We propose the architecture, UX direction, and delivery milestones.
Build
We ship in short cycles with demos, reviews, and tests.
Launch & support
We release carefully, then keep improving with a shared backlog.
Related work
Selected projects that used this service area.
Frequently asked questions
Do we need RAG or fine-tuning?
Most product features that need answers grounded in your documents start with RAG. Fine-tuning fits style or structured output patterns when you have enough examples. We help you choose based on the job, not the trend.
How do you handle private company data?
We design access controls, retention rules, and retrieval over approved sources. Provider and hosting choices follow your security requirements; NDAs are available before you share sensitive material.
How do you keep answers reliable?
We add evaluation for critical prompts, logging for failures, and clear refusal behavior when retrieval finds nothing relevant. Quality is treated as an engineering concern, not a one-time prompt tweak.
Can you add AI to an existing app?
Yes. We usually start with one high-value flow inside your current product, then expand once retrieval, auth, and monitoring are solid.
What drives the cost of an AI feature?
Scope of the feature, data preparation, evaluation, and ongoing model usage. Use /estimate for a rough band; we quote after understanding your data and success criteria.
Ready to talk through scope?
Book a free consultation or get a directional project estimate.