Generative AI Integration
We embed generative AI into the workflows that matter, co-pilots, RAG over your private data, agents that take action, with the guardrails, evals and observability enterprises require.
GenAI products that survive a Monday morning.
A demo can pass on cherry-picked prompts. A product has to handle the user who hasn't read the manual, the regulator who reads everything and the long tail of edge cases that don't fit a slide. Our GenAI approach is built around that reality: ground every answer, evaluate every release and keep the cost curve under control as adoption grows. The result is a system you can put your name on, not a science fair project.
Grounded by default
Retrieval, citations and validators are non-negotiable. If we can't cite it, we don't say it.
Eval-driven releases
Golden sets, LLM-judge evals and red-team suites gate every deploy. We catch regressions before users do.
Cost as a design constraint
Caching, model routing and quantization are baked in from v1. We commit to a fixed unit-cost SLA, not a hopeful estimate.
Why GenAI is no longer optional in 2026.
The window between "GenAI is a moonshot" and "GenAI is table stakes" closed faster than any technology shift before it. The leaders are already on their second iteration.
McKinsey 2025, adoption has crossed the chasm. The competitive question is no longer "if" but "how good".
Bloomberg Intelligence. Every category from search to support to document review is being rewritten and the budget is following.
Repeatedly observed across studies (Microsoft, GitHub, BCG). Teams without copilots are competing with teams that have them.
Generative AI Integration services we offer.
Each item below is a discrete, measurable workstream we own end-to-end, with senior engineers, real timelinesand the test coverage to back it up.
Retrieval-augmented generation (RAG)
Vector + hybrid retrieval over your private corpus, with re-rankers and citation enforcement. The answers are grounded and the user knows where they came from.
Domain fine-tuning (LoRA / SFT / DPO)
Take a base model from 60% to 90%+ on your domain with parameter-efficient tuning. Cheaper than prompt-stuffing, faster than full retraining.
Multi-agent orchestration
Plan-and-execute, tool-using agents that call your APIs, read your DBs and take actions, coordinated by a supervisor with safety budgets.
Prompt registries & evals
Version-controlled prompts. Golden-set + LLM-judge evals. We catch quality regressions before users do.
Safety, PII, jailbreak guardrails
Layered defenses: input filters, output validators, citation enforcement, content classifiers, all logged and auditable.
Latency/cost optimization
Caching, model routing, speculative decoding, quantization. We routinely halve token cost without touching quality.
We're fluent in your stack.
Vendor-agnostic by design. We pick the right tool for the problem in front of us, not the one our partner discounts apply to.
Real engagements. Real numbers.
Internal research co-pilot with cited answers
A regulated bank deployed a RAG-backed analyst assistant, every answer ships with primary-source citations and was approved by compliance.
Six reasons enterprises run Generative AI Integration with Infivit.
Built for the 2026 reality of Generative AI Integration: the actual buyer pain, the actual technical constraints and the actual outcomes that matter, not generic AI talking points.
Hallucination rate kept below 2%.
Retrieval grounding, eval harness, output validators and reranking. Your CFO and compliance team can sign off on what your GenAI tells customers.
3× retrieval precision over naive embeddings.
Hybrid search (BM25 + dense), reranking and chunking strategy tuned per corpus. Your assistant cites the right document, not a hallucinated paraphrase.
Layered guardrails, sandboxed tools, full audit.
Input filters, output filters, scoped tool access and jailbreak-pattern detection. Adversarial users can't pivot your assistant into doing what it shouldn't.
Redaction, tokenization, on-prem inference.
Sensitive flows run on private endpoints with PII redaction at the edge. Vendor LLMs only ever see scrubbed, tokenized payloads, never your raw data.
LoRA, QLoRA, RLHF and DPO fine-tuning.
When prompt engineering plateaus, we fine-tune. Cheaper than long contexts, more accurate on your domain and yours to own forever.
Golden sets + LLM-as-judge + human review.
Every prompt change runs through a regression suite of golden examples. Quality drift caught before it reaches your users, never after a Twitter screenshot.
The questions you were already going to ask.
Got a generative ai integration problem?
Let's ship the fix.
A 30-minute call with one of our senior engineers, no slideware, no scoping doc. You leave with a concrete view of what the first 30 days look like.