Model APIs for application development and comparison.
AI-native professional services studio
Make AI work.For your business.
Your partner across the entire AI lifecycle. We research, architect, build, evaluate, secure and operate AI systems that turn enterprise AI ambitions into measurable business outcomes.
Abstract diagram of an AI delivery system connecting strategy, agent systems, evaluations, observability, security, and LLMOps.
AI StrategyAgent SystemsEvaluationsObservabilityAI SecurityLLMOps
Fig. 01 — Unified delivery system
- 01Research
- 02Architect
- 03Build
- 04Evaluate
- 05Secure
- 06Operate
From AI ambition to execution
AI is changing how businesses operate. We help them make that change real.
Enterprises don't need another collection of AI tools. They need the expertise, engineering and operational capabilities to turn those tools into reliable business systems.
Starts asA model
Has to becomeA system
A model is a starting point. Value depends on the problem, the system around the model, and the people who remain accountable.
What we do
Everything your enterprise needs to build with AI.
From your first AI use case to complex autonomous systems operating at scale, we bring together the expertise required across the entire AI lifecycle.
01
Beyond software
We don't just build AI for businesses. We use AI to deliver the work itself.
Traditional professional services are being reimagined. We combine AI agents, specialized workflows and human expertise to deliver complete business services with greater speed, consistency and efficiency.
Scoped delivery can be engaged now, with a specialist still accountable. Future service lines are directions we are preparing — not operational offerings.
Legal Services
Scoped delivery
We will scope and deliver this with specialists and AI workflows. It becomes a managed operation only when an engagement explicitly says so.
Legal work is volume plus judgment. We design workflows that draft, compare, and retrieve, while qualified counsel remains responsible for the advice.
Services
- Contract drafting and review
- Legal research
- Document analysis
- Litigation support
- Legal operations
Potential AI workflows
- Intake a contract, extract obligations, and compare them with a playbook.
- Retrieve authorities and prior positions for a research question, with citations a lawyer can check.
- Sort a document set for a matter and route exceptions to a reviewer.
Human expertise
- Counsel accountable for legal judgment.
- Reviewers who accept or reject suggested edits.
- Matter and operations leads who define the playbook.
Business outcomes
- Faster first drafts without handing judgment to a model.
- More consistent review against an explicit playbook.
- A clearer record of what was checked and by whom.
How we work
From first question to production-grade AI.
Six stages, from the first useful question to a system someone can run. Validate covers evaluation and security before anything is called production.
01
Discover
Understand business challenges, existing systems and AI opportunities.
- Map the workflow, the systems around it, and the decision that would change.
- Name the operator, the risk, and what a useful outcome would look like.
- Separate problems that are ready to build from problems that need research first.
Flexible expertise. Complete ownership.
Five ways to work together. The model follows the job: a decision, a build, an operation, a domain service, or a team beside yours.
Independent by design. Connected by technology.
Tools and platforms we work with. A listing is not a partnership, certification, or endorsement.
Models & Providers
Model APIs considered against the same task harness.
Multimodal and long-context options in vendor evaluations.
Open-weight models when ownership of the stack matters.
Open and hosted models in build-versus-buy studies.
Low-latency inference options where speed is a requirement.
Agent Frameworks
Graph-based agent flows when control flow must be explicit.
Multi-agent coordination patterns we will adopt only with a test harness.
Agent tooling when the surrounding platform is already a fit.
Retrieval and agent orchestration in document-heavy systems.
Infrastructure
Cloud infrastructure for deployment, data, and operations.
Cloud infrastructure, including model-adjacent platform services.
Cloud infrastructure for enterprises already standardized there.
Workload orchestration when the platform team expects it.
Portable packaging for services, evaluators, and jobs.
Observability & Evaluations
Evaluation and observability tooling we can implement, not a required standard.
Tracing and prompt operations, often alongside open telemetry.
Open evaluation and trace workflows for teams that want to own the data.
Tracing and datasets when the LangChain-adjacent stack is already in use.
The convention we prefer so traces are not trapped in one vendor.
Vector Databases
Managed vector search for retrieval systems.
Vector search with a preference for portable deployments.
Lightweight vector storage for early systems and tests.
Vector search when latency and self-hosting are part of the brief.
Development
Primary language for model applications, evaluation, and pipelines.
Services where concurrency, deployment, and operational simplicity matter.
Product interfaces, gateways, and full-stack delivery.
Service boundaries for model-backed applications.
Interfaces for copilots, review tools, and operational consoles.
Security
Structured attempts to misuse a specific system before users depend on it.
Controls on retrieval, tools, and outputs, designed with the architecture.
Cases built to break assumptions about instructions, data, and tools.
A repeatable way to rate findings and retest them after a fix.
Why Adanla
One partner. The entire AI lifecycle.
End-to-End AI Expertise
Strategy, architecture, engineering, evaluation, security, and operations sit in one practice, so early decisions are made by people who also have to run the system.
Vendor-Agnostic Recommendations
Models, clouds, and frameworks are chosen for the problem. The stack stays replaceable as the market moves. A recommendation is not a reseller arrangement.
Engineering-First Approach
The work is shaped by people who build, instrument, and operate. A workshop can start an engagement. It is not the thing we leave behind.
Human Expertise + Machine Intelligence
Specialists stay accountable for judgment, domain context, and risk. AI takes the volume, the retrieval, and the steps that should be consistent.
Outcome-Driven Delivery
An engagement is framed around a business system that can be evaluated: a workflow, a control, a cost, a cycle, a risk reduced. Activity is not the result.
Continuous Optimization
Launch begins the measurement. Evaluations, traces, and operating reviews keep the system honest after the project energy fades.
Insights
Thinking beyond the AI hype.
Notes from the studio on research, engineering, evaluation, and the operating reality of enterprise AI. Not client results.
About
Built for the next era of enterprise intelligence.
Adanla is an AI-native professional services company that helps enterprises research, strategize, architect, build, evaluate, secure, deploy and operate AI-powered solutions.
We don't just help businesses understand AI. We help them put AI to work.
The studioLet's build
Have an AI problem worth solving?
Whether you're exploring your first AI initiative or scaling autonomous systems across your enterprise, let's figure out what comes next.