Language models that
get your business.

We build production-grade LLM integrations grounded in your data — with model selection, RAG pipelines, prompt engineering, and full observability baked in from day one.

Book a discovery call See how we build

3 models

evaluated against your use case before any commitment

34%

hallucination rate on domain content without proper grounding

60%

avg cost reduction vs. unoptimized LLM deployment

2–4w

to a grounded, production-ready LLM system

The Problem

Most LLM deployments ship fast. Then quietly fall apart.

Ungrounded models hallucinate. Unmanaged prompts drift. Unoptimized pipelines burn budget. These aren't edge cases — they're the default outcome of skipping the engineering.

34%

hallucination rate when deploying GPT or Claude on domain-specific content without retrieval grounding or fine-tuning

6 weeks

average time teams waste evaluating models instead of shipping — because they picked the wrong one first

4–8×

higher cost of unoptimized LLM deployments vs. properly engineered pipelines running the same workload

70%

of in-house LLM integrations are rebuilt within 18 months due to architecture decisions made under deadline pressure

Capabilities

Six LLM engineering disciplines
deployed across your stack.

RAG Systems

Zero domain hallucination

Ground every LLM response in your proprietary data — internal docs, CRM, knowledge base, product catalog — eliminating hallucination on domain content entirely.

Model Selection & Evaluation

Right model, first time

We test GPT-4o, Claude 3.5, Gemini 1.5, and open-source alternatives against your actual use case, data, and latency requirements before recommending a single provider.

Fine-Tuning & Domain Adaptation

Domain-accurate outputs

Adapt base models to your terminology, tone, domain logic, and output format — so every response is on-brand, accurate, and consistent at any volume.

Prompt Engineering & Management

Drift-proof prompts

Design, version, test, and deploy prompts like code — with fallback chains, output validation, edge case coverage, and a management layer that prevents prompt drift.

LLM Orchestration

Multi-step reasoning

Chain models, tools, retrieval systems, and memory across multi-step reasoning tasks using LangChain, LlamaIndex, or custom orchestration — for workflows that require more than one inference call.

Observability & Cost Optimization

60% avg cost reduction

Monitor every inference call for latency, token usage, output quality, and cost. Identify waste, route cheaper models to low-stakes queries, and cut LLM spend without touching accuracy.

The Process

Audit to production in 6–8 weeks.

Every phase has a defined deliverable you can hold us to. No vague milestones. No scope creep.

01

Audit & Define Scope

Weeks 1–2

We analyze your use case, data landscape, and success criteria — mapping exactly what the LLM needs to know, how it will be grounded, and what "good output" means before writing a single prompt.

Deliverable

Use case spec + grounding strategy + success criteria

02

Model Selection & Architecture

Weeks 2–3

We run structured benchmarks of 2–3 candidate models against your real data and latency requirements — then design the full system architecture: retrieval layer, prompt stack, memory, and cost guardrails.

Deliverable

Model recommendation + system architecture

03

Build, Ground & Evaluate

Weeks 3–6

We build the RAG pipeline or fine-tuning workflow, engineer the prompt system, and evaluate against your acceptance criteria using real inputs — including adversarial and edge case testing.

Deliverable

Grounded LLM system + evaluation report

04

Deploy with Observability

Weeks 6–8

Every LLM integration ships with logging, latency tracking, output quality monitoring, cost alerts, and a fallback system for low-confidence responses — before the first real user sees it.

Deliverable

Production deployment + observability dashboard + runbooks

Industry Applications

LLM systems built for your industry's actual language and data.

Finance & Legal

  • Contract analysis agents extracting structured data from unstructured legal text at 99%+ accuracy
  • Risk summarization models distilling 200-page reports into structured executive briefs in seconds
  • Regulatory document parsing identifying compliance gaps across policy changes automatically
  • Credit memo drafting from structured financial data with configurable templates and approval workflows

Healthcare

  • Clinical note summarization reducing documentation time for clinicians by 65% per patient
  • Patient communication drafting generating clear, compliant outreach from structured clinical data
  • Medical coding assistance suggesting ICD/CPT codes from clinical notes with accuracy auditing
  • Prior authorization letter generation from EHR data reducing submission time from 45 to 8 minutes

SaaS & Technology

  • Knowledge base automation generating and updating help articles from product changelogs
  • Support deflection systems resolving 72% of tickets using grounded responses from your documentation
  • Feature documentation generation producing structured spec docs from engineering tickets and PRDs
  • Internal search transformation converting keyword queries into semantic document retrieval

E-Commerce & Operations

  • Product description generation maintaining brand voice across 100k+ SKU catalogs
  • Supplier contract review flagging non-standard terms against policy templates in under 60 seconds
  • Customer feedback synthesis categorizing and summarizing thousands of reviews into structured product intelligence
  • RFP response generation pulling from case studies, capability docs, and pricing data into draft proposals

Scroll to explore more industry applications.

Discuss your integration
Proof of Work

Real systems. Real results.

HealthcareCelara Health
“HIPAA-compliant AI that doesn't slow your clinicians down.”

Celara's intake process required clinicians to manually extract and enter data from patient-submitted forms — taking 45 minutes per patient. We built a HIPAA-compliant LLM pipeline that reads intake documents, pre-populates structured chart fields, flags high-risk presentations, and routes paperwork — reducing intake from 45 minutes to 8 minutes without touching clinical decision-making.

RAG PipelineHIPAA ComplianceDocument Intelligence

8 min

patient intake (down from 45)

— Dr. Priya Nambiar, Chief Digital Officer

Why Acsenix

Grounded. Monitored. Cost-optimized. Built to last.

Unlike wrapper tools that hallucinate on your domain or in-house builds that break when models update — we engineer LLM systems you own, with architecture that adapts as the model landscape evolves.

Off-the-shelf LLM wrappers

Prompt the API. Ship the hallucination.

  • No retrieval grounding
  • Hallucination-prone on domain content
  • No prompt management
  • Vendor lock-in to one model
  • No cost optimization
  • No output quality monitoring

In-house integration

Expensive to build. Breaks when models update.

  • Months to production
  • Prompts managed in spreadsheets
  • Single model dependency
  • No fallback or safety layer
  • Technical debt accumulates fast
  • Rebuilt when APIs change
Our model

Acsenix

Grounded. Monitored. Cost-optimized. Built to last.

  • RAG-grounded — zero domain hallucination
  • Model-agnostic — swap providers without rewriting
  • Prompt system versioned like code
  • Full output quality observability
  • 60% avg cost reduction vs. unoptimized
  • Ongoing AMC — adapts as models improve
Start with Acsenix
FAQ

Questions we hear on every discovery call.

Straightforward answers — no sales spin.

Ask us directly
Get Started

Stop shipping LLMs
that guess at your domain.

Book a 30-minute discovery call. We'll map exactly where grounded LLMs would replace manual processes in your org — and what that's worth in hours and dollars.

Book discovery call hello@acsenix.space

Free LLM audit included. No commitment required.