GenAI assistants & copilots
Assistants for customers and teams, embedded in your product, Slack or Teams, grounded in your knowledge.
You getA production assistant with evaluations and analytics
- OpenAI
- Claude
- Azure OpenAI
- Gemini
Artificial Intelligence
AI at work.People in control.
We design, build and run production AI: assistants, agents and document intelligence wired into your workflows, grounded in your data and governed from day one.
Agent trace
- 30+
- AI systems in production
- 70%
- Typical tasks fully automated
- 100%
- Decisions traceable to a source
- 10 wks
- Typical time to a live pilot
The problems we solve
If this sounds familiar, your AI is stuck in pilot.
Most AI problems are not model problems. They are about fit, data, trust and ownership. Here is what we hear from engineering and product leaders, and what changes.
“Our AI pilot never made it to production.”
The demo worked. There were no evaluations, no integration and no owner.
What changes
Production engineering from day one: evaluations, integration, monitoring and a named owner.
“We don’t know where AI actually fits.”
Every team has ideas and nobody can rank them.
What changes
A fit-and-value assessment that ranks use cases, and says no where rules or people do better.
“The answers sound right, but they aren’t.”
Confident answers, no sources and no way to check them.
What changes
Retrieval grounded in your data, with citations, guardrails and accuracy measured every release.
“Legal and security keep blocking it.”
PII, data residency and unclear accountability.
What changes
Responsible-AI controls: PII masking, access policies, audit trails and human review where stakes are high.
“Inference costs are growing faster than the value.”
The biggest model for every call, and nobody watching spend.
What changes
Model routing, caching and right-sizing, with cost per task tracked like any other metric.
What we offer
Six capabilities, one production AI practice.
Start where the value is clearest. Each capability ships on its own and shares the same evaluation, security and operating foundations.
Agentic workflows
Agents that complete multi-step work across your systems, with tools, approvals and clear limits.
You getAutomated workflows with human checkpoints
- LangGraph
- Semantic Kernel
- MCP
- Temporal
Document intelligence
Read, classify and extract from contracts, claims, invoices and forms, with confidence scores and review.
You getStructured data from documents, ready to act on
- Azure AI Document Intelligence
- Textract
- LLM extraction
Retrieval, knowledge & semantic layer (RAG)
Search and answers over your own content, plus a semantic layer that gives AI one governed definition of every metric, with permissions respected and sources cited.
You getOne knowledge and metrics layer your assistants and agents trust
- pgvector
- Pinecone
- Azure AI Search
- dbt Semantic Layer
- Cube
Predictive ML & decisioning
Forecasting, scoring and anomaly detection that drive decisions, with explanations people can check.
You getModels in production with reason codes and monitoring
- scikit-learn
- XGBoost
- PyTorch
- MLflow
AI governance & LLMOps
Evaluations, red-teaming, monitoring and responsible-AI controls that keep systems safe as they change.
You getEval suites, guardrails, audit trails and cost dashboards
- Langfuse
- Promptfoo
- Guardrails
- OpenTelemetry
C247 AI ·CLEAR Framework
The method that turns an AI idea into a production system.
CLEAR connects the business case, data reality, AI fit, risk, architecture, and delivery plan before development begins.
How we deliver
From first prompt to AI that earns trust.
We prove value on one workflow with real users before scaling, and measure accuracy, safety and cost at every release.
-
01
CLEAR discovery
Rank use cases, map data and systems, set accuracy, safety and cost targets.
2–4 weeks -
02
Proof of value
An evaluation set and a working prototype on real examples.
Accuracy baseline -
03
Pilot in the workflow
Live with real users, guardrails and human review in place.
Value proven -
04
Scale & integrate
More workflows and users on the same foundations.
Workflow by workflow -
05
Run & improve
Quality, drift and cost monitored; feedback turned into improvements.
Ongoing
01
The team that joins
Senior people, accountable for the outcome
- AI/ML lead accountable for the system
- AI and full-stack engineers models, agents, product
- Data engineer retrieval and pipelines
- Delivery lead reports in your rituals
02
Quality & safety gates
Checked on every release, not once
- Evaluation suite run on every change
- Red-teaming and prompt-injection tests
- PII and access review including data residency
- Cost per task checked before release
03
After launch
Watched, improved and yours to run
- Monitoring quality, drift and cost
- Feedback loop from human reviewers
- Runbooks and model cards in your repo
- Hand-over or a managed run
“Think first. Then build.” is not a delay before delivery. It is how we make delivery more dependable.
Technology and partners
The right models for your use case and risk.
We are model-agnostic and design for change: swap a model without rewriting the system. These are the platforms we deliver on most often, layer by layer.
Cloud
Build, deploy, and scale secure cloud infrastructure.
- AWS
- Terraform
- Docker
- Azure
- GCP
- Kubernetes
Stack decision principle: We do not force a fixed toolset. We choose technology after CLEAR clarifies the data, workflow, security posture, integration reality, operating cost, and ownership model.
Client impact
Building things that will last.
Featured work across verticals: proof that the method works beyond one flagship project.
View all work
Regional insurance carrier
Settling simple claims the same day with document AI and human review
Residential property manager
Filling vacancies faster with tenant matching and digital leasing
Online learning provider
An adaptive learning platform that keeps every learner on track
Regional logistics operator
Turning delivery data into on-time decisions across the fleet
Precision components manufacturer
Predicting machine failures before they stop the line
Multi-specialty clinic network
Cutting patient intake time in half with a clinically reviewed assistant
Digital lending platform
Same-day lending decisions with explainable risk scoring
Specialty retailer
One AI-assisted platform for every customer conversation
01 / 08
Connect
Clarity before code.
We will help clarify the workflows, data, users, risks, and build path before your team commits engineering budget.
- Workflows
- Data
- Users
- Risks
- Build path