AI Context Engineering Services
Context engineering, as practiced by Kodexo Labs, is the discipline of structuring what an AI model sees at inference: its memory, retrieved data, tool schemas, and reasoning steps. Done well, it makes language models answer from real company data, not from memorized text.
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TRUSTED BY ENTERPRISES
We treat context as an engineering problem, not a clever prompt. Our team maps your data sources, memory needs, and reasoning paths, then builds the retrieval and orchestration layers that keep answers accurate.
Core Capabilities
Memory architecture that keeps agents aware across long, multi-step conversations.
Retrieval pipelines that ground answers in your own operational data.
Chain-of-thought reasoning design for accurate, explainable, and auditable model decisions.
Tool and function schema design that lets agents act reliably.
Multi-agent orchestration that shares context cleanly without losing the thread.
Evaluation harnesses that catch context failures before your users do.
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AI-powered products
On Clutch and Elite AI Firm
Client retention rate
Expert Team shipping AI
Context Engineering AI, Built Like Software
Context engineering isn't a clever prompt you paste once. It's system design: deciding what the model retrieves, remembers, and reasons over at every step. We build that machinery so your AI stays accurate as usage grows.
Window & Memory
The context window is the model's working memory. We decide exactly what fills that token budget so nothing important ever gets dropped mid-conversation.
We keep past chats so your agent can recall each user across many visits.
We compress old turns into short notes so the model keeps room for input.
Is Your AI Guessing Instead of Actually Knowing?
Most AI projects stall because the model never sees the right context. We fix that layer so your answers hold up in production.
Context Engineering in Production
Diesel Laptops
Diesel Laptops' fleet technicians spent more time searching diagnostic records than fixing trucks. We built an AI search system that finds the right answer across 160,000 technical records in seconds. It runs self-hosted inside this Inc. 5000 company's own AWS VPC. Lookup time dropped 85%, so mechanics get back under the hood much faster.
85%
Faster Lookup
160,000
Records Searched
AWS VPC
Self-Hosted
Extensiv
Extensiv's operations team waited on engineers for every data question they had. We built an agentic system on LangGraph that reads plain-English questions and answers them straight from their own operational database. This Inc. 5000 logistics company now self-serves at 90%+ accuracy across 207 tables and 4 databases, with no engineering ticket required anymore.
90%+
SQL Accuracy
207
Tables
04
Databases
IFPG
IFPG's chatbot was handing prospects HTML-broken answers across 1,000+ franchise listings, and leads were dying at the very first click. We rebuilt the reasoning layer with chain-of-thought prompting, which walks the model through its logic before replying. HTML errors hit zero, and answer accuracy climbed 85% for the largest franchise network in North America.
1,000+
Listings
85%
Accuracy Lift
Zero
HTML Errors
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- HIPAA-compliant patient contextStructured history across sessionsClinical note generationZero re-query workflows
Context Engineering Built For The Realities Of Your Industry
Every vertical stores knowledge differently, and every regulator expects something distinct. Our engineers study how your sector handles records, memory, and retrieval, then shape the context layer around those constraints so answers hold up under real scrutiny.
Ready To Put Your Company Data Behind Every AI Answer?
Most AI projects stall when models answer from memorized text instead of your systems. A short scoping call shows where retrieval, memory, and orchestration would move the needle. Bring us your hardest question.
Compliance Standards We Architect To, Not Just Talk About
Regulated buyers do not approve an AI system on the strength of a policy PDF. They approve it on controls they can see. Every multi-agent system we ship isolates agent code inside a Docker sandbox, a walled-off container that keeps generated code away from your live systems, and logs every agent action for audit. That is production-grade AI you can put in front of a compliance officer.
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
COPPA
NIST AI RMF
FERPA
PCI-DSS
ISO 42001
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
COPPA
NIST AI RMF
FERPA
PCI-DSS
ISO 42001
Why Teams Choose Kodexo Labs To Engineer The Context Behind Their Production Models
Context engineering rewards teams who have shipped it under load, not just diagrammed it. We have done both across regulated, high-volume systems. These four reasons explain what that experience buys you when accuracy actually matters.
Hallucinations Cut By Structure
IFPG runs the largest franchise network in North America. Its bot gave broken HTML and shaky text. We rebuilt each step as a chain-of-thought context. HTML errors hit zero. Accuracy rose 85 percent.
Context Design That Scales
Extensiv needed plain-English answers from 207 tables in four databases. We built the state and memory layer in LangGraph, so it tracks what it has read. Accuracy holds above 90 percent, without SQL.
Regulated Context That Persists
SmartMedHx runs the patient interview and writes the note, then carries that history to the next visit under HIPAA. 42 providers use it each day over 493 interviews. Memory design guards the record.
Research Depth, Not Guesswork
Context Engineering is a research field, not a feature. Our approach is grounded in academic rigor and applied ML expertise, combining doctoral-level research methods with hands-on, Microsoft-certified experience in machine learning and computer vision.
Ready To Engineer Context That Actually Holds
Most AI agents fail not because the model is weak, but because the context feeding it is thin, stale, or disorganized. We rebuild that layer so your agents reason on the right information every time. Our PhD-level team maps the context gaps and shows a concrete path forward.
Recognized, Not Self-Declared
Clutch verifies every review before these badges appear, so the recognition reflects client outcomes rather than marketing spend. Top AI, machine learning, and chatbot rankings come from it.
Overcoming The Toughest Context Engineering Challenges
Context engineering breaks in predictable ways. Retrieval floods the window, stale data drives hallucinations, and single-turn prompts collapse across multi-step agent runs. Kodexo Labs treats every failure as an engineering problem, not a prompt tweak, and fixes each one structurally.
Every tool listed is in active production on a Kodexo Labs.
Every framework, runtime, and cloud service named here is running on a live client product right now. No theoretical stack, no resume keywords, no tools added for marketing weight.
How We Engineer Context, Phase By Phase
Discovery And Audit
Stakeholder interviews, data-source mapping, and a full audit reveal exactly how information reaches the model today. This closely mirrors the discovery-sprint approach that shaped Extensiv's rollout across all four databases and 207 source tables.
Context Architecture Design
Next we design the context architecture: retrieval strategy, memory layers, chunking rules, and prompt structure. You receive a documented blueprint showing how every piece of information flows into and out of each reasoning agent.
Build And Integration
Our engineers build the retrieval pipelines, wire memory stores, and integrate tools against your live systems. Real data sources connect inside a secured environment, then each component gets validated before wiring the full workflow.
Evaluation And Red-Teaming
Model outputs face evaluation against a labeled test set, then red-team testing against adversarial prompts, edge cases, and stale-data traps. Accuracy, grounding, and failure rates get measured before anything approaches your real production users.
Handoff And Monitoring
At handoff you receive documentation, evaluation dashboards, and monitoring hooks that track drift, latency, and grounding in production. Kodexo Labs stays available for tuning as your data, traffic, and agent behavior all evolve over time.
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Context Engineering FAQs
Context engineering is the discipline of structuring exactly what an AI model sees before it reasons. Rather than rewording prompts, Kodexo Labs designs retrieval, memory, and grounding, often using RAG (retrieval-augmented generation), so the model works from correct data. For Extensiv, this approach let operations teams query 207 database tables in plain English at more than 90 percent accuracy.