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AI & GenAI Solutions

95% of enterprise GenAI pilots return nothing. Ours are built to be the 5%.

MIT's Project NANDA found 95% of enterprise generative AI pilots deliver zero measurable return - most stall on ungrounded output, no cost ceiling, or content nobody signed off on. We build the other kind: retrieval grounded in your own sources, spend capped before you scale, and a person accountable for what publishes.

Start a Project See the Receipts Compare Us Honestly
Copilots
RAG
LLM Integration
Governance
Observability
GenAI Delivery Status Live Use-case discovery Aligned Data readiness Prepared RAG + agents Built Governance Guarded Monitoring Live

The Receipts

The track record, not the pitch deck.

Every number below sits on a public profile - verify it yourself before you talk to us.

74M+Downloads on one product we engineered
3M/dayVisitors handled by a platform we built - Clutch-verified
4.9Clutch rating across 5 verified reviews
66Unsolicited client reviews, all platforms
0Reviews we ever asked for
101Contracts completed on Upwork, Top Rated

The Anatomy

One pipeline, five checkpoints, every time it generates.

Not a decision loop - a production line. Content moves left to right, and nothing publishes without a citation and a human check.

Briefthe ask, briefed Generation Enginedrafts · cites · refines Source Libraryyour data, cited Brand & Compliance+ human review Publishaudit-logged

The Range

Eight kinds of GenAI systems this process has produced - not a menu, a track record.

Each one went through the same pipeline you just saw - briefed, generated, grounded, and reviewed before it shipped.

Foundation Layer

01
Copilots

Enterprise Copilots

Assistive interfaces embedded in your existing product, grounded in your data and scoped to what the user is allowed to see.

Ground › Assist › Verify
02
Retrieval

RAG Knowledge Systems

Enterprise search and Q&A over your documents, with citations on every answer instead of confident guessing.

Ingest › Retrieve › Cite
03
Generation

Content Generation Systems

Drafting and production pipelines for proposals, reports, and communications, with a human review step before anything ships.

Draft › Review › Publish
04
Governance

Governance & Observability Layer

Evaluation harnesses, access control, audit trails, and cost dashboards wrapped around every system above.

Evaluate › Monitor › Govern

Content Production Layer

05
Marketing

Marketing & Brand Content Generation

On-brand copy, ad variants, and product descriptions generated at scale, checked against your voice guide before anything ships.

Brief › Generate › Brand-Check
06
Multimodal

Multimodal Creative Generation

Image, video, and audio assets generated from a creative brief, with usage rights and source provenance tracked on every asset.

Brief › Generate › Rights-Check
07
Personalization

Personalization & Recommendation Content

Emails, product copy, and on-site content that adapts per segment or user, generated and refreshed without a manual rewrite each time.

Segment › Generate › Personalize
08
Localization

Multilingual Localization Engines

Content and documentation generated and adapted for each market's language and tone, not machine-translated as an afterthought.

Source › Translate › Localize

See It Work

Three generations, mid-run - not mockups.

Same grounding and brand discipline, three different outputs. Each one is paused or logged exactly where a well-built GenAI system should be.

1 Marketing Copy Generation
EcoFlow Bottle · Instagram Ad Generating
"Hydration that doesn't cost the planet."
"Your daily refill, guilt-free."
"Less plastic. More you." Selected
Brand-voice check: 3/3 scored · 1 flagged for energy, resolved on variant 3
2 Multimodal Creative Generation
ProductShoot_EcoFlow · 4 variants rendered
1:1
4:5
9:16
16:9
98% brand palette match Usage rights pending sign-off
3 Personalized Email Generation
Segment: cart abandoners, 3+ days · avg cart $92
Generic

"Check out our new arrivals."

Personalized

"Your cart's waiting - plus 10% off if you finish today."

Personalization Score82 / 100
Sent · conversion tracked per variant

What We Build

Six disciplines, non-negotiable, every build.

Skip one and the system ships broken - it doesn't matter how good the model is.

01

LLM Integration & Prompting

Secure API integration, prompt architecture, and fallback handling that turns a raw model call into a product feature - not a demo.

Non-negotiable
02

RAG Grounding

Every answer traces back to a document you can open yourself - not what the model half-remembers about your company.

Non-negotiable
03

Tool & Data Integration

Pulls product data, brand assets, and past-approved content into the generation pipeline itself - so every draft starts from your source material, not a blank prompt.

Non-negotiable
04

Guardrail Engineering

Policy checks, PII redaction, and approval gates on anything consequential - built in from day one, not bolted on after an incident.

Non-negotiable
05

Evals & Red-Teaming

Every output batch is scored against a rubric - factual accuracy, brand tone, and hallucination rate - before it ships and after every model swap.

Non-negotiable
06

Observability & Cost Control

Latency, token spend, and quality tracked per request - so a cost spike or quality drop shows up before your finance team asks about it.

Non-negotiable

How It Runs

A ten-week build, laid out end to end.

  1. Discover the Use Case01 · Wk 1

    One prompt-to-value workflow and the metric that proves it, written down before week two.

  2. Design the Guardrails First02 · Wk 1-2

    Data boundaries, access scopes, and approval gates before any model code.

  3. Build & Ground03 · Wk 2-6

    LLM integration, RAG, and tool wiring - working increments every sprint.

  4. Score It04 · Wk 6-8

    The eval suite scores groundedness and cost-per-query before real traffic ever touches it.

  5. Go Live, Gated05 · Wk 8-10

    Live on real queries, with a human approving anything the model isn't confident about.

  6. Earn Autonomy06 · Ongoing

    Autonomy expands only as the eval score earns it - never on a timeline.

Compare Us Honestly

Four ways to get GenAI built. One row-by-row look.

Filter to the comparison that matters to you, or leave it on all four and judge for yourself.

Strength Trade-off Depends

Typical AI Vendor

  • What survives the demoA chatbot that hallucinates on real documents
  • Grounding & RAG qualityGeneric retrieval, rarely tuned to your data
  • Data & tool accessBroad API keys, hope for the best
  • Cost controlToken spend discovered on the invoice
  • Quality after a model updateVibes
  • Who you talk toA salesperson, then a rotating bench
  • ReviewsSolicited testimonials
  • Your riskDeposit up front

Freelance AI Consultant

  • What survives the demoDepends entirely on one person's skill and availability
  • Grounding & RAG qualityWorkable if they've built RAG before, untested if not
  • Data & tool accessWhatever access you grant them personally, hard to audit after
  • Cost controlHourly - cost grows with iteration and debugging
  • Quality after a model updateRarely retested unless you pay for it
  • Who you talk toOne person - no backup if they're unavailable or move on
  • ReviewsPlatform reviews only, hard to verify AI-specific work
  • Your riskHourly and open-ended - cost grows with iteration

In-House AI Team

  • What survives the demoA slow build while your team learns LLM engineering from scratch
  • Grounding & RAG qualityPossible, but competes with your team's other priorities
  • Data & tool accessUsually broad, since your own team already has internal access
  • Cost controlSalary and infra cost, whether or not the pilot works out
  • Quality after a model updatePossible, but competes with your team's other priorities
  • Who you talk toYour own team, but they're learning on your dime
  • ReviewsN/A - internal build, no external track record
  • Your riskSalary and hiring cost, whether or not the pilot works out
Our Approach

Zetrixweb

  • What survives the demoA pilot on real documents, human-gated from day one
  • Grounding & RAG qualityRetrieval you can inspect, citations on every answer
  • Data & tool accessLeast-privilege scopes per task, nothing more
  • Cost controlToken and latency budget shown before you scale, not after
  • Quality after a model updateAn eval score, before and after, in writing
  • Who you talk toThe engineers who build it - interview them first
  • Reviews4.9 on Clutch, 66 reviews, zero ever asked for
  • Your riskFree 7-day trial; walk away with the work
Start a Project

The Zetrixweb column is verifiable: Clutch profile, Upwork record, and the engagement terms we publish.

Held To In Writing

Grounded, capped, and yours to keep.

These aren't sales promises - they're written into the statement of work before a single prompt ships.

Grounded or We Fix It Free

If a shipped feature answers without a source it can cite, we patch it before the invoice goes out - no charge, no argument.

Token Cost Ceiling

You get a modeled cost-per-query range before anything reaches production. Blow past it by more than 15% and we re-architect at our expense.

You Own the Prompts and Embeddings

Every prompt template, fine-tune, and vector index lives in your repo, under your account - nothing sits locked in an agency-only workspace.

The Arsenal

Any model, any framework - swap without a rewrite.

The generation and retrieval stack behind every build, model-agnostic by design.

Foundation Models

AnthropicAnthropic
OpenAIOpenAI
GeminiGemini
Meta LlamaMeta Llama
MistralMistral
CohereCohere

Enterprise Model Hosting

Microsoft AzureAzure OpenAI
AWSAWS Bedrock
Google CloudGoogle Cloud

Frameworks & Orchestration

LangChainLangChain
LlamaIndexLlamaIndex

Memory & RAG

PineconePinecone
WeaviateWeaviate

Already Carrying Load

GenAI already live in production, not slideware.

Two of the eight types above, already running for paying clients - not illustrations.

Enterprise CopilotsMedNurse

LLM copilot for care teams, grounded in patient records

Zero ungrounded outputs to date
→
Governance & ObservabilityPrivacyLock

LLM-powered clause extraction and risk flagging

Every flag cites its source clause
→
Before You Ask

The questions that decide this. Straight answers.

A GenAI feature generates or retrieves content on request - a copilot answering a question, a drafting assistant. An agent goes further: it plans multi-step actions, calls tools, and completes an outcome under policy guardrails. We build both, often in the same system.

Yes. We integrate via secure APIs and a retrieval layer on top of your existing stack - most integrations don't require touching your core product architecture, just the surfaces where AI assists the user.

Policy checks, PII redaction, role-based access, audit trails, and evaluation workflows on every engagement, with human approval gates on anything consequential. This is standard on every build, not an enterprise-tier upsell.

A scoped GenAI pilot typically lands in the $8K-$25K range depending on integration count and eval depth, quoted fixed after a free scoping call, and runs 6-10 weeks end to end. Specialist hours start at $15/hour, dedicated squads from $1,000/month. Model-inference cost is billed separately by usage - you'll see the projected number in the eval report before anything reaches production.

Anthropic, OpenAI, Gemini, Llama, Mistral, and Cohere models; Azure OpenAI, AWS Bedrock, and Google Cloud hosting; LangChain and LlamaIndex orchestration; Pinecone and Weaviate vector stores - plus eval harnesses and observability on every engagement. You're never locked to one vendor.

Look at three things: we've never solicited a review yet sit at 4.9 on Clutch across 66 unsolicited reviews, you get to interview the engineers who'll actually build your system before signing anything, and you can run a free 7-day trial on a real use case before spending a rupee.

Nothing routes through a shared, multi-tenant model or vector store - your data and embeddings stay inside your own environment. Access is least-privilege and scoped per task, never one blanket key, and you can revoke or narrow it any time. Every retrieval and generation call logs back to the run that made it.

The person on the call is the person who builds it.

Bring the use case and leave with a grounding plan, a pilot scope, and a real number - whether or not you hire us.

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