We pick the two or three use cases that clear a real ROI bar and set aside the ones that demo well and return nothing. You get a sequenced roadmap, not a wish list.
We wire LLMs and agents into the systems you already run, your CRM, your warehouse, your ticketing tool, so the model acts on live data instead of a stale export.
The unglamorous majority of the work. We clean the pipelines that feed the model, then stand up the monitoring and deployment so it keeps working after launch instead of drifting.
Governance that holds up under audit, plus the privacy and access controls regulated buyers in finance and healthcare require before anything ships.
We train the people who now work alongside the system, redraw who owns what, and keep a human in the loop wherever the decision carries risk.
After rollout, we run the monitoring, approval workflows, and model upkeep so the system stays accurate and governed as your data and usage change.
We help you run OpenAI models inside your own Azure tenant, with the private-networking, data-residency, and governance controls enterprise buyers require. Architecture, prompt and retrieval design, and cost guardrails included.
Multi-model foundation-model builds on Amazon Bedrock for teams already on AWS. We design the retrieval layer, evaluate model choice against your workload, and keep spend visible per feature.
ML pipeline automation and Gemini-native builds on Vertex AI. We connect your data, stand up the training and serving pipeline, and put evaluation and monitoring around the model.
The plumbing that keeps a model working in production: automated deployment, drift and quality monitoring, retraining triggers, and rollback. This is the part of AI readiness that survives launch.
Bounded-scope agents with audit logs and human approval gates.
Grounded answers from your documents, with vector DB and citations.
Deployment, drift and quality monitoring, and retraining after launch.
An AI consulting firm helps a company move from AI pilots to systems that run in production and pay for themselves. In practice that means selecting the use cases worth building, integrating models into your existing systems, getting the data ready, standing up MLOps and governance, and training the people who work alongside the result.
Use-case selection, solution architecture, data and pipeline preparation, model or agent build, evaluation, and the MLOps and governance needed to run it. We scope the work against fixed milestones so you see the deliverables and the number before you commit.
Azure OpenAI, AWS Bedrock, and Google Vertex AI. We are vendor-neutral and build on the platform you already run, or recommend one based on your existing cloud, your data residency needs, and the models your workload actually requires.
MLOps is the operations layer that keeps a model working after launch: automated deployment, monitoring for drift and quality, retraining triggers, and rollback. Yes, we provide it. It is the part of AI readiness that survives launch, and skipping it is why most pilots quietly degrade.
We work inside your access controls and audit requirements, document how each system makes decisions, and put approval gates on anything that touches a customer or a system of record. For regulated buyers we build the governance and evidence trail before the model ships, not after.
A scoped first production agent typically ships in 6 to 10 weeks. Broader programs take longer, but we sequence the work so you get a governed, usable result early rather than waiting for everything at once.
It depends on the use cases, the state of your data, and how much needs to run in production. We scope every engagement against fixed deliverables and milestones before any work starts, so you see the number before you commit. Start with an audit and we will give you a plan.
Senior-led AI consulting: strategy, agent and LLM builds on Azure OpenAI, Bedrock, and Vertex AI, plus MLOps and governance. From pilot to production.
Strategy, agent and LLM builds, MLOps, and governance. From pilot to production that pays off.
We do not start with a year-long strategy program. We sequence the work so you get a governed, usable result early and build from there.
Score the data and pick the two or three use cases that clear an ROI bar.
Governance, the semantic layer, and the pipelines the model actually needs.
Governance and managed operations as it scales across the business.
There is no flat rate. These are the factors that move the effort and the price.
One scoped agent is a different engagement than a program across several workflows.
Clean, governed data shortens the build. Fragmented sources add readiness work before any model.
Running a model in production with monitoring and retraining is more than a proof of concept.
Regulated environments need documentation, approval gates, and an evidence trail built in.
You have AI pilots that impressed the room but never reached production.
You want models built on your own cloud with governance that holds up under audit.
You need the data and MLOps underneath the model, not just a demo.
The numbers disagree by definition: see Semantic Layer Engineering.
Your bigger problem is AI and cloud spend: see Cloud & AI Cost Optimization.
The large firms sell the same AI work weighted toward a broad transformation program. Here is where a senior-led boutique is different for a data and AI build.
Senior practitioners end to end. The person who scopes it runs it.
Fixed fee per phase, scoped and agreed before any work starts.
Time-and-materials or a padded fixed bid, with change orders.
We recommend against building or migrating when the math says no.
Not every company is ready for a full AI build on day one. Pick the entry that matches where you are, and we scope from there.
A fixed-scope assessment of your data, use cases, and AI readiness. You get a prioritized roadmap and a scoped plan before any build begins.
A defined engagement with fixed deliverables and milestones: use-case selection, the model or agent build, and the MLOps to run it. You see the number before you commit.
We run it after we ship it. Monitoring, evaluation, governance, and change control so the system keeps working and stays compliant.
We hand it off. Documentation, training, and pairing so your team operates and extends the AI systems without us in the loop.
Most AI work stalls in the gap between a demo that impressed the room and a system that runs in production. We close that gap. Strategy, agent and model builds on the platform you run, the data work underneath, and the governance to operate it, from one senior-led team.
An AI consulting firm takes a company from AI pilots to production systems that pay for themselves. Thinklytics does the full path: selecting the use cases worth building, integrating models into your existing systems, getting the data ready, running MLOps and governance, and enabling the people who work alongside the result.
Vendor-neutral by default. We build on Azure OpenAI, AWS Bedrock, or Google Vertex AI depending on your cloud, your data residency needs, and the models your workload requires, and we run the MLOps that keeps it working.
Start with an audit. We review your data, your use cases, and your readiness, then give you a prioritized plan before any work begins.