AI Development Company for US Businesses
AI that does real work for your business.
RAG, agents, document processing and AI features inside existing products. Built with evals, guardrails and a cost ceiling—not a vibe. The kind of AI you can sign a contract against.
Four shapes of AI-shaped pain.
If one of these sounds familiar, AI probably has a contribution to make. If none of them do, we'll tell you that too—on the first call.
- 01
Repetitive manual work
Tagging, extracting, summarising, routing. The kind of work nobody learned a degree for. AI is great at the boring 80% so your team can own the interesting 20%.
- 02
Slow customer support
Median first response on email is 12 hours; on chat it should be 30 seconds. A grounded assistant answers the 60% that are obvious, escalates the rest.
- 03
Data collected but never used
Two terabytes of tickets, calls and PDFs—and nobody opens them. We turn them into the brain of an assistant that actually knows your business.
- 04
Not knowing where AI even helps
Most of the work is choosing the right problem. We run a 1-week feasibility audit before any line of code—and tell you when AI is not the answer.
Seven shapes of AI we put into production.
Not every problem is a chatbot. We pick the shape that actually moves the metric.
AI assistants & chatbots
Grounded chat assistants for support, sales, internal Q&A. Backed by your docs, your tone, your guardrails. Not another GPT wrapper.
Workflow automation & agents
Multi-step agents that read, decide and act—within tight tool scopes. Triage, drafting, follow-up, reporting.
Document & data processing
Pull structured data out of PDFs, invoices, forms, contracts. Human-in-the-loop where it matters; full auto where it doesn’t.
Recommendations & personalization
Editorial + collaborative recos for stores, content sites and SaaS. Trained on your data, owned by you.
Predictive analytics
Churn, propensity, demand—built as predictions inside the products and dashboards your team already opens.
AI features inside existing products
Summarise, rewrite, classify, search—dropped into the screens your users already use. Latency-budgeted, eval-gated, cost-capped.
Assistants grounded in your knowledge base
RAG done seriously: chunking strategy, hybrid search, citation-first answers, refusal-when-unsure behaviour, evals.
Seven stages. One eval suite.
Quality is a number, not a vibe. Every step of our approach is anchored to a metric you can read in a board meeting.
Use-case discovery & feasibility
01A one-week audit. We map the top five candidates, score them on impact and feasibility, and tell you—honestly—whether AI is the right tool for each.
Data readiness
02Inventory your sources, audit quality, decide on chunking + embedding strategy. The 80% of an AI project that nobody films a demo of.
Prototype / POC
03A scoped POC in two to four weeks. Live, evaluable, not a slide deck. Real users on it before week five.
Integration
04Into your product surface, your CRM, your ops console. We do not believe in standalone "AI portals" unless that is literally the product.
Evaluation & guardrails
05Eval suites with ground-truth examples, refusal tests, jailbreak tests, latency + cost budgets. Quality is a number, not a vibe.
Deployment
06A staged rollout: shadow → 5% → 25% → 100%. Kill-switches, fallback to deterministic logic, on-call playbook.
Monitoring & improvement
07Observability for prompts, citations, costs, refusals. A monthly review cadence with model + prompt changes shipped behind feature flags.
Ask NioBot a question. Streaming response.
A grounded mini-assistant wired to /api/ai/chat/stream on this site. It only answers about NioDelta's services—and refuses what it doesn't know.
The boring slide we made interesting.
Trust is the conversion lever for AI work. We design for it on day one—not as a launch checklist.
No training on your data
We only use vendors with no-training contractual clauses. Your data is yours. Period.
PII redaction in-flight
Names, phone numbers, IDs and free-text are scanned before they ever reach a model. Configurable per field.
Citations or it didn’t happen
Every grounded answer carries citations to the source chunks. Users can audit. Reviewers can verify.
Numbers we’d sign a contract against.
Medians across the last 7 AI engagements (2024–25). The boring metrics, on purpose—latency, accuracy, savings.
AI automation for business operations.
Not every AI project is a chatbot. Often the best return is removing a repetitive step from work your team already does.
- 01
Document processing
Read invoices, purchase orders, KYC documents and forms, extract the fields, and push them into your ERP or CRM with a human check on low-confidence items.
- 02
Enquiry & lead handling
Classify incoming WhatsApp, email and website enquiries, draft a reply from your own price list and policies, and route the lead to the right salesperson.
- 03
Support automation
Answer routine customer questions from your documents and order data, and hand over to a person with the full context when the question is not routine.
- 04
Reports & summaries
Turn daily sales, production or attendance data into a short written summary sent to managers each morning.
- 05
Workflow agents
Multi-step tasks such as follow-ups, reconciliations and data entry across tools, run with approval steps, logs and spending limits.
Things we're asked before the call.
Two layers. First: the assistant only sees the documents you gate it to—nothing else. Second: we use providers with no-training clauses, encrypt at rest, and offer self-hosted embeddings or open-source models when policy demands it.
Less than you think, if it is built right. We use citation-first prompts, retrieval-grounded answers, refusal heuristics, and an eval suite that catches regressions before deploy.
Yes. We deploy to your AWS / GCP / Azure account. For sensitive workloads we run open-source models (Llama, Mistral, Qwen) on your own GPUs.
We set a per-conversation cost ceiling in the design phase and stay under it. For a grounded support assistant, $0.01–$0.04 per resolved query is typical at production volumes.
Whichever one matches the job. Claude Sonnet / Opus for reasoning-heavy work, GPT-5 family for tool use + structured output, Gemini for very large context, open models when policy or cost demands it.
A scoped POC is 2–4 weeks. A production-grade assistant with evals, guardrails and monitoring is 8–12 weeks.
Where AI fits with the rest of the build
Tell us what AI is supposed to do.
A 30-minute WhatsApp call with a partner. We'll be honest about whether AI is the right tool for your problem.