What AI/ML capabilities do you support?

We handle recommendation systems, NLP (classification, extraction, chat), computer vision, time-series forecasting, and intelligent automation. We evaluate LLM integration where it adds genuine value.

How long until we see results?

PoC to production typically takes 6–12 weeks. Simple integrations (e.g., hosted LLM APIs) can be faster. Complex custom models with MLOps take longer.

AI Engineering

Ship AI workflows
that improve real operations

Turn a use case into a monitored AI workflow with data readiness, evaluation, review logic, and product-safe rollout.

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See AI Case Studies

Feasibility and evaluation before rollout
Human review and guarded rollout

2–4wFeasibility & Data Audit

6–12wPilot to Rollout

Eval-ledSafety & Monitoring

Production AI workflow

Senior-led

Production AI workflow

Define the task. Add guardrails. Roll out safely.

We scope the workflow around data readiness, evaluation, fallback logic, and monitored rollout into a real product or operations system.

Feasibility

Baseline and acceptance thresholds before model spend.

Guardrails

Evaluation, review, and fallback paths inside the workflow.

Productionization

Serving, monitoring, and operating cost planned with integration.

Workflow scope

Use-case framing and data audit
Model or provider selection
Prompt design and orchestration

Service overview

AI workflows built for measurable operations and product outcomes.

Practical ML/AI engineering for product teams. From model selection to production MLOps. PoC in 6-10 weeks. Senior ML engineers. We design AI workflows around data readiness, model fit, evaluation, review logic, and monitored rollout into real...

Production AI delivery

AI delivery focused on workflow change, measurable outcomes, and production-safe implementation.

Use caseAutomation, copilots, retrieval, and decision support

GuardrailsEvaluation sets, approvals, fallbacks, and monitoring

DeliveryIntegrated into APIs, ops workflows, and product surfaces

From model selection and MLOps to inference at scale — we handle the full lifecycle so you focus on product outcomes, not infrastructure complexity.

This is how product teams ship AI into live workflows without turning the roadmap into a science experiment.

What this means for you:

Measurable task-level success criteria before model work begins
Retrieval, orchestration, and business rules planned with the product flow
Evaluation sets, human review, and fallback routes designed up front
Production APIs, monitoring, and audit-ready rollout

This is how product teams ship AI into live workflows without turning the roadmap into a science experiment. We build AI systems that your compliance and security teams will approve. Delivered by senior ML engineers. Full model ownership transfer.

What disciplined rollout changes

The workflow stays clearer when evaluation, guardrails, and rollout are designed before launch pressure builds.

Feasibility

When it starts as a demo

Start with model enthusiasm and vague success criteria.

When it is built for rollout

Define the business task, baseline, and acceptance thresholds before model spend.

Data readiness

When it starts as a demo

Assume the source data is usable once the prototype works.

When it is built for rollout

Audit source quality, labels, access constraints, and drift risk before rollout.

Guardrails

When it starts as a demo

No review step or fallback route when confidence drops.

When it is built for rollout

Human review, thresholds, policy rules, and safe fallback paths are part of the workflow.

Included in the engagement

WHAT'S INCLUDED

Use-case framing, data audit, and success-metric definition
Model or provider selection across LLM, retrieval, ML, or rules-first approaches
Prompt/state design, orchestration, and product-logic integration
Evaluation sets, sampled QA, approvals, and fallback handling
Serving, monitoring, alerting, and rollout support
Runbooks, iteration plan, and handover-ready delivery artifacts

Best fit

Where this model is strongest.

Product teams adding copilots or workflow intelligence

Search, classification, summarization, recommendations, or assistant flows that need to fit into a real product surface.

Operations teams reducing manual review

Document processing, decision support, routing, and exception handling where human review still matters.

Teams moving from prototype to monitored rollout

You already proved user value and now need evaluation, APIs, guardrails, and maintainable operations.

Common AI workflow situations

What teams usually need operationalized.

Recommendation Systems

Product, content, and user recommendations with real-time inference.

NLP & Text Intelligence

Classification, extraction, summarization, and chat-based features.

Computer Vision

Image classification, object detection, and visual search.

Why disciplined AI delivery matters

Why AI projects fail without product and engineering discipline

The risky part is rarely the model alone. It is unclear task definition, weak data assumptions, missing evaluation, and no operational fallback.

Feasibility

Failure mode

Start with model enthusiasm and vague success criteria.

Production-safe outcome

Define the business task, baseline, and acceptance thresholds before model spend.

Data readiness

Failure mode

Assume the source data is usable once the prototype works.

Production-safe outcome

Audit source quality, labels, access constraints, and drift risk before rollout.

Guardrails

Failure mode

No review step or fallback route when confidence drops.

Production-safe outcome

Human review, thresholds, policy rules, and safe fallback paths are part of the workflow.

Productionization

Failure mode

Inference is bolted on after the demo succeeds.

Production-safe outcome

Serving, caching, monitoring, logging, and operating cost are planned with the product integration.

What this protects

Evaluation, guardrails, and rollout design keep the workflow usable after the demo stage.

We scope AI like a production workflow: clear task definition, measurable evaluation, guarded rollout, and monitored operation.

See how we deliver AI Request a Call

AI delivery model

How we deliver production AI

The work starts with task-level value and data readiness, then moves through evaluation, orchestration, guarded rollout, and monitored iteration.

Feasibility before model spend

We validate the use case, baseline, and data constraints before recommending a provider, model, or training track.

Human review where failure matters

Approval steps, fallback logic, and confidence thresholds are built into the workflow when outputs affect customers or operations.

Operational ownership after launch

Evaluation, drift monitoring, prompt or model iteration, and runbooks stay part of the delivery plan after rollout.

Use-case and data audit

We define the task, baseline, acceptance thresholds, source systems, and the data constraints that matter before recommending any model or provider path.

workflow map
data-readiness assessment

Baseline, model selection, and evaluation

We test the right approach for the task — retrieval, hosted LLMs, trained models, or rules-first automation — and create evaluation sets that can be rerun over time.

evaluated baseline
model/provider recommendation

Orchestration, review, and integration

We wire the workflow into your product or operations stack with retrieval, prompts, business rules, approvals, fallback behavior, and production APIs.

orchestrated workflow
reviewed output path

Monitor, tune, and hand over

After rollout we track quality, latency, drift, and operating cost, then hand over with runbooks and an iteration plan for prompts, models, or thresholds.

monitoring dashboard
runbooks

AI stack

AI delivery stack built around the workflow

We choose the provider, orchestration layer, evaluation setup, and serving path around data shape, latency, review needs, and long-term operability.

Selection principle

Model & provider layer

Hosted models, retrieval-backed flows, or trained pipelines are chosen against accuracy, latency, cost, and control requirements — not trend bias.

OpenAI API, TensorFlow, Python, LangChain

Selection principle

Retrieval & orchestration

Workflow logic includes retrieval, prompts, rule checks, caching, business-state handling, and API boundaries around the model output.

LangChain, FastAPI, PostgreSQL, Redis

Selection principle

Evaluation & rollout

Quality checks, traceability, monitoring, and safe rollout matter as much as model choice once the workflow reaches production.

GitHub Actions, Docker, AWS, Google Cloud

Model & provider layer

4 tools

Choose the right model or provider path for the workflow and acceptance threshold.

OpenAI API

TensorFlow

Python

LangChain

Retrieval & orchestration

4 tools

State, retrieval, prompts, and rules live around the model rather than inside it.

LangChain

FastAPI

PostgreSQL

Redis

Serving & workflow integration

4 tools

Production APIs and internal automations are built for traceability, latency, and reliability.

FastAPI

Docker

Node.js

GraphQL

Monitoring & operations

4 tools

Rollout includes observability, infrastructure, and repeatable delivery around the AI workflow.

GitHub Actions

AWS

Google Cloud

Docker

Pricing and delivery

Clear budget, delivery scope, and next steps.

USD estimates based on data readiness, workflow complexity, latency requirements, and how much evaluation, orchestration, and rollout support the system needs.

01Estimate

$7K – $30K+

Typical investment (USD)

Pricing covers feasibility, model or provider selection, orchestration, guardrails, and production rollout — not just a demo or notebook prototype.

Fixed-price discovery and pilot phases. Production rollout is scoped from validated use cases and real integration requirements.Discovery usually takes 2–4 weeks before we lock the rollout scope and the production estimate.

02Timeline

POC 6–10 weeks

Typical delivery window

The exact window depends on scope depth, integration complexity, and the level of handoff or hardening required before launch.

03What is included

What the engagement is designed to protect.

Feasibility and baseline quality defined before model spend
Evaluation, guardrails, and review paths built into rollout
Operational monitoring, handover, and iteration plan included

04Delivery scope

What your team receives as part of delivery.

Senior AI engineers embedded into the workflow definition
Prompt/model/provider selection with product and operations context
Production APIs, orchestration, and fallback logic
Monitoring, runbooks, and a post-launch iteration path

Next step

Use a strategy call to turn this into a realistic scope.

We can clarify the scope, delivery risks, and the next step before we put a firm proposal in front of you.

Request a CallNo obligation. Clear next-step recommendation.

AI delivery FAQ

Frequently Asked Questions

Common questions about AI integration projects.

Who owns the trained model?

You own 100% of the trained model, weights, and all artifacts. We deliver the full model package including training code, inference code, and deployment configurations.

How do you handle data privacy?

Your data stays in your infrastructure. We can work with anonymized data, synthetic data, or under strict data governance agreements. We never train on customer data without explicit consent.

What if the model doesn't perform well?

We start with a PoC phase specifically to validate feasibility. If baseline experiments show the problem isn't ML-tractable with available data, we'll tell you before significant investment.

How is pricing structured?

Pilot engagements are usually fixed-price ($7K – $12K typical). Production projects are scoped after feasibility validation and the real workflow integration requirements are clear. Ongoing maintenance is optional and structured as monthly retainers.

Scope the workflow

Scope an AI workflow that can operate in production

Work with a senior AI delivery team that treats evaluation, guardrails, and monitored rollout as part of the product — not afterthoughts.

A quick review of your current delivery situation, an honest fit check, and a recommendation on the next technical step.

Outcome-led scoping
Evaluation before rollout
Production rollout planning
Reply within 1 business day

Ship AI workflows that improve real operations

AI workflows built for measurable operations and product outcomes.

AI delivery focused on workflow change, measurable outcomes, and production-safe implementation.

The workflow stays clearer when evaluation, guardrails, and rollout are designed before launch pressure builds.

Feasibility

Data readiness

Guardrails

WHAT'S INCLUDED

Where this model is strongest.

Product teams adding copilots or workflow intelligence

Operations teams reducing manual review

Teams moving from prototype to monitored rollout

What teams usually need operationalized.

Recommendation Systems

NLP & Text Intelligence

Computer Vision

Why AI projects fail without product and engineering discipline

Feasibility

Data readiness

Guardrails

Productionization

Evaluation, guardrails, and rollout design keep the workflow usable after the demo stage.

How we deliver production AI

Use-case and data audit

Baseline, model selection, and evaluation

Orchestration, review, and integration

Monitor, tune, and hand over

AI delivery stack built around the workflow

Model & provider layer

Retrieval & orchestration

Evaluation & rollout

Model & provider layer

Retrieval & orchestration

Serving & workflow integration

Monitoring & operations

AI and intelligent automation examples

Ceramic Portal Web

Healthstur

SCAPITAL

Manage My Dojo

Clear budget, delivery scope, and next steps.

What the engagement is designed to protect.

What your team receives as part of delivery.

Use a strategy call to turn this into a realistic scope.

Frequently Asked Questions

Scope an AI workflow that can operate in production

Ship AI workflows
that improve real operations