# Adanla > Your AI Implementation & Professional Services Partner - **Category:** AI-Native Professional Services - **Entity type:** AI_NATIVE_PROFESSIONAL_SERVICES - **Version:** 1.0 - **Status:** available We don't just help businesses understand AI. We help them put AI to work. ## Company profile Adanla is an AI-native professional services company that helps enterprises research, strategize, architect, build, evaluate, secure, deploy and operate AI-powered solutions. ## Company description We go beyond traditional AI consulting and software development. We partner with enterprises across the complete AI lifecycle, helping them adopt AI internally and transform their business operations through intelligent systems. ## Service categories - Enterprise AI Enablement - AI-Native Industry Services ## Detailed service catalogue ### AI Strategy & Research Understand where AI can create value before investing in implementation. Engagement model: AI Advisory & Consulting Services: - Enterprise AI readiness assessments - AI maturity assessments - AI opportunity discovery - AI product and market research - AI technology landscape research - Competitive AI tool comparisons - Vendor and model evaluations - Build vs Buy vs Integrate assessments - AI roadmap and ROI consulting - AI adoption strategy Business problems addressed: - Tools get purchased before the business problem is sharp enough to build against - Leadership has no shared picture of maturity, risk, or which workflows should change first - Vendor and model claims cannot be compared on the tasks that actually matter Example enterprise use cases: - An executive team choosing a first wave of funded AI use cases - A product organization deciding whether to build, buy, or integrate a copilot - A strategy group that needs a roadmap tied to cost, revenue, or risk — not a tool inventory Typical deliverables: - Readiness and maturity assessment - Opportunity map and prioritized roadmap - Vendor, model, and build-versus-buy evaluation - Adoption strategy with an explicit ROI framing ### AI Architecture & Engineering Design and build intelligent systems that solve real enterprise problems. Engagement model: Project-Based Implementation Services: - LLM application development - Agent architecture and engineering - Multi-agent systems - RAG architecture and implementation - Enterprise AI copilots - Internal AI assistants - Model selection and integration - Fine-tuning and custom model integration - AI workflow automation - Enterprise application and API integration Business problems addressed: - Pilots that impress in a demo and cannot reach the systems of record - Unclear boundaries between models, tools, data, permissions, and users - Agent designs that only work on the happy path Example enterprise use cases: - An internal assistant grounded in approved company knowledge - A controlled multi-step agent embedded in an existing business workflow - A product copilot integrated with the application and its APIs Typical deliverables: - Solution and integration architecture - A working LLM, RAG, or agent system - Model integration with evaluation hooks from the start - Handover notes for the team that will operate it ### AI Evaluations & Quality Engineering Make AI systems measurable, reliable and production-ready. Engagement model: Managed AI Services, or a fixed evaluation project Services: - AI Evaluations-as-a-Service - LLM benchmarking - Model performance comparisons - Agent evaluation - RAG evaluation - Hallucination and factuality testing - Relevance and accuracy evaluation - Custom evaluator development - Human-in-the-loop evaluations - Continuous evaluation pipelines - AI quality assurance Business problems addressed: - No agreed definition of a good answer for the tasks that matter - Quality regressions that appear only after a model or prompt change - Release decisions driven by anecdotes instead of a harness Example enterprise use cases: - Comparing models on the enterprise's own tasks, not a public leaderboard - Gating a release on factuality, relevance, and tool-use checks - Installing a continuous evaluation pipeline next to deployment Typical deliverables: - Evaluation plan and task datasets - Benchmark and model comparison - Custom evaluators and a human-review design - Release criteria and a repeatable pipeline ### AI Observability & LLMOps Bring visibility, control and operational excellence to AI applications. Engagement model: Managed AI Services Services: - AI observability architecture - LLM tracing and instrumentation - Logging and monitoring - Token usage and cost monitoring - Latency and performance optimization - Prompt management and versioning - Experimentation and deployment pipelines - LLMOps implementation - Model routing and gateway integrations - Production alerting and incident management Business problems addressed: - No trace from a user outcome back to prompts, retrieval, tools, and models - Cost and latency discovered on the invoice instead of in the design - Prompt and model changes with no history and no rollback Example enterprise use cases: - Instrumenting a production copilot before it is widely released - Standing up prompt versioning, experiments, and a deployment path - Routing models through a gateway with budgets, fallbacks, and alerts Typical deliverables: - Observability architecture - Tracing, logging, and cost views tied to real requests - Prompt management and a deployment pipeline - Alerting and an incident path the team can actually run ### AgentOps & Simulation Test, simulate and continuously improve autonomous AI agents. Engagement model: Managed AI Services, or a focused engineering project Services: - Agent architecture reviews - Agent simulation environments - Synthetic scenario generation - User and environment simulations - Multi-agent testing - Agent behaviour analysis - Tool-calling validation - Agent performance benchmarking - Agent reliability engineering - Continuous agent evaluation Business problems addressed: - Agents that pass a scripted demo and fail when the case is messy - No safe environment in which to rehearse tool use and handoffs - Behaviour changes that nobody measured until a user found them Example enterprise use cases: - Simulating users, data gaps, and environment failures before launch - Testing multi-agent handoffs and the contracts between them - Benchmarking whether tool calls are valid, complete, and allowed Typical deliverables: - Agent architecture review - Simulation environment and scenario set - Behaviour, reliability, and tool-use report - A continuous agent evaluation loop ### AI Security, Governance & Compliance Make AI systems secure, responsible and enterprise-ready. Engagement model: AI Advisory & Consulting, then project or managed security Services: - AI security audits - LLM vulnerability assessments - Prompt injection testing - Jailbreak testing - LLM red teaming - Sensitive data leakage testing - AI guardrails - Model risk assessments - AI governance frameworks - Regulatory readiness assessments - Responsible AI consulting Business problems addressed: - Prompt injection, tool abuse, and data leakage have not been tested - Model risk has a policy owner and no control inside the product - Governance documents that the running system does not enforce Example enterprise use cases: - Red-teaming an assistant before employees are asked to rely on it - Designing guardrails for a workflow that can see sensitive data - Preparing a regulatory readiness view for a specific use of AI Typical deliverables: - Security and vulnerability assessment - Red-team findings with a retest path - Guardrail and governance design tied to the architecture - Model risk and regulatory readiness memo ### AI Infrastructure & AIOps Build and operate the infrastructure powering enterprise AI. Engagement model: Project-Based Implementation or a Dedicated AI Engineering Team Services: - AI infrastructure architecture - GPU and cloud infrastructure optimization - Model deployment and serving - Inference optimization - AI gateway integration - Scalability and reliability engineering - AI infrastructure monitoring - Automated operations - Cost and resource optimization - Disaster recovery Business problems addressed: - Inference cost or latency that makes an otherwise good system unusable - A deployment path that cannot scale, recover, or be observed - Infrastructure frozen around a single demo environment Example enterprise use cases: - Serving a model inside a measured latency and cost budget - Designing cloud and GPU footprint around a real workload - Adding monitoring, scaling, and a recovery path before broad rollout Typical deliverables: - Infrastructure architecture - Deployment, serving, and gateway setup - Cost and performance review - Operating monitors and a disaster-recovery plan ## Industry specializations Industry services are not all operational. Scoped delivery can be engaged. Future service lines are directions being prepared. ### Legal Services Phase: 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 Related capabilities: - ai-strategy-research - ai-architecture-engineering - ai-evaluations - ai-security-governance ### Finance & Accounting Phase: 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. Finance runs on documents, reconciliations, and controls. We apply AI where the work is repetitive and keep accountants responsible for the numbers that get reported. Services: - Bookkeeping - Financial reporting - Reconciliation - Tax preparation - Audit support - Financial document processing Potential AI workflows: - Classify and extract financial documents into a review queue - Prepare reconciliation candidates and surface breaks for an accountant - Assemble reporting packs from approved source data, with every figure traceable Human expertise: - Accountants who sign off classifications and adjustments - Controllers responsible for the reported numbers - Tax and audit specialists for positions that require judgment Business outcomes: - Less manual handling of routine documents - Breaks found earlier in the close - Workpapers that show the source of a figure Related capabilities: - ai-architecture-engineering - ai-evaluations - ai-observability-llmops - ai-security-governance ### Insurance Phase: Future service line. A direction we are preparing. We can advise and prototype. We do not present it as an operational service today. Claims, underwriting, and policy work are document-heavy and tightly regulated. This is a future service line: we will help design the workflows, and we do not run them as a standing operation today. Services: - Claims processing - Underwriting support - Risk assessment - Policy administration - Claims investigation support Potential AI workflows: - Extract claim facts from first notice and supporting documents - Assemble an underwriting brief from submissions and third-party data - Flag policy inconsistencies for a specialist before a decision is issued Human expertise: - Claims handlers who decide coverage and settlement - Underwriters who own risk selection - Investigators for cases that leave the standard path Business outcomes: - A designed path from document intake to a human decision - Fewer incomplete files reaching a specialist - Evaluation criteria agreed before any workflow is scaled Related capabilities: - ai-strategy-research - ai-architecture-engineering - ai-evaluations - ai-security-governance ### Human Resources & Recruitment Phase: 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. Hiring and HR operations mix coordination with decisions that affect people. We automate the preparation and the paperwork, and we keep humans responsible for selection and employee judgment. Services: - Talent sourcing - Candidate screening - Recruitment operations - Interview coordination - HR documentation - HR operations automation Potential AI workflows: - Structure a role intake and produce a sourced longlist for a recruiter to edit - Screen against published criteria and record why a candidate moved forward - Draft HR documents and route them for a human owner to approve Human expertise: - Recruiters who own outreach and candidate judgment - Hiring managers who make the selection - HR partners accountable for policy and employee decisions Business outcomes: - Less coordinator time lost to scheduling and status chasing - Screening criteria that are explicit and reviewable - Documentation that stays consistent with policy Related capabilities: - ai-architecture-engineering - ai-evaluations - ai-security-governance - agentops-simulation ### Healthcare Phase: Future service line. A direction we are preparing. We can advise and prototype. We do not present it as an operational service today. Clinical and administrative work carries patient risk and strict obligations. Healthcare is a future service line. We do not offer clinical services, and we do not claim an operational healthcare practice today. Services: - Clinical documentation - Medical administration - Healthcare billing - Insurance claims processing - Patient coordination Potential AI workflows: - Draft administrative summaries from approved source notes for a clinician to edit - Prepare billing packets and highlight missing codes or documents - Coordinate routine patient logistics while escalating anything clinical Human expertise: - Clinicians responsible for any clinical content - Billing specialists who approve codes and claims - Care-coordination staff who own patient communication Business outcomes: - A responsible design for administrative load, with clinical judgment untouched - Clear escalation rules before a workflow is tried - Security and evaluation requirements defined up front Related capabilities: - ai-strategy-research - ai-security-governance - ai-evaluations - ai-architecture-engineering ### Banking & Financial Services Phase: Future service line. A direction we are preparing. We can advise and prototype. We do not present it as an operational service today. Banking operations sit inside heavy control environments. This is a future service line: useful to design with a partner bank, not something we present as a live managed operation. Services: - KYC operations - Loan processing - Document verification - Financial research - Transaction analysis Potential AI workflows: - Assemble a KYC file and list discrepancies for an analyst - Extract loan-file data and check it against a checklist before credit review - Prepare a research brief on a name or market from approved sources Human expertise: - KYC and compliance analysts who own the decision - Credit officers responsible for the lending call - Research leads who verify anything that will be relied upon Business outcomes: - File preparation separated from the regulated decision - Exceptions made visible instead of silently resolved - A control design that a risk team can review before scale Related capabilities: - ai-security-governance - ai-evaluations - ai-architecture-engineering - ai-strategy-research ### Compliance & Regulatory Phase: 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. Compliance work is monitoring, evidence, and submissions. We use AI to gather and structure that material. The compliance owner remains accountable for the position the organization takes. Services: - Regulatory research - Compliance monitoring - Audit preparation - Documentation - Regulatory submissions Potential AI workflows: - Track a regulatory change and summarize what may affect a named control set - Collect evidence for an audit request and map it to the question asked - Draft a submission narrative from approved facts for counsel or compliance to edit Human expertise: - Compliance officers who own the interpretation - Audit leads who decide what evidence is sufficient - Counsel where the output becomes a formal position Business outcomes: - Change monitoring that reaches the right owner - Audit preparation with a visible evidence map - Drafts that stay tied to approved source material Related capabilities: - ai-strategy-research - ai-evaluations - ai-security-governance - ai-observability-llmops ### Sales & Marketing Phase: 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. Commercial teams lose time to research, list work, and campaign operations. We build the intelligence and the execution layer, and we leave the offer, the relationship, and the claim with the people accountable for them. Services: - Market intelligence - Lead research - Sales operations - Competitive intelligence - Campaign execution Potential AI workflows: - Build an account brief from public and approved internal sources - Research a lead list against a stated ideal-customer definition - Prepare campaign variants and a launch checklist for a marketer to approve Human expertise: - Sellers who own the conversation and the forecast - Marketers accountable for the claim and the brand - Revenue operations leads who define the process Business outcomes: - Better prepared conversations, not unsupervised outreach - Research that can be checked back to a source - Campaign operations with a human approval step Related capabilities: - ai-architecture-engineering - agentops-simulation - ai-evaluations - ai-strategy-research ### Logistics & Supply Chain Phase: Future service line. A direction we are preparing. We can advise and prototype. We do not present it as an operational service today. Logistics coordination depends on documents, exceptions, and partners outside the building. This is a future service line we are preparing, not a network we operate today. Services: - Shipment documentation - Procurement operations - Logistics coordination - Supply chain intelligence Potential AI workflows: - Check shipment documents for missing fields before a coordinator sends them - Compare a procurement request with policy and preferred suppliers - Summarize disruption signals for a planner who decides the response Human expertise: - Logistics coordinators who own the shipment - Procurement managers who award the buy - Planners accountable for inventory and service levels Business outcomes: - Cleaner documents before they leave the building - Exceptions raised against a written policy - Intelligence briefs a planner can challenge Related capabilities: - ai-architecture-engineering - ai-observability-llmops - agentops-simulation - ai-strategy-research ### IT & Software Operations Phase: 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. IT and software operations already live in queues, tickets, and code. We apply AI to triage, testing, and analysis, with engineers still responsible for what ships and what changes in production. Services: - Application support - QA and testing - Technical support - Code analysis - IT service management Potential AI workflows: - Triage a ticket, gather context, and propose a next action for an agent or engineer - Generate and run test ideas against a change, then report what was not covered - Review a code path for risk signals a maintainer can confirm Human expertise: - Engineers who approve code and production changes - Support leads who own customer communication - QA owners who decide whether a release is fit Business outcomes: - Faster context on a ticket or a change - Tests that expand coverage without replacing the release owner - Analysis that stays reviewable by the people who maintain the system Related capabilities: - ai-architecture-engineering - ai-evaluations - ai-observability-llmops - ai-infrastructure-aiops ## Technical capabilities ### Practice areas - **AI Strategy & Research.** Decide where AI creates value before you fund the build. - **AI Architecture & Engineering.** Design and build systems that survive contact with the enterprise. - **AI Evaluations & Quality Engineering.** Make quality measurable before a system is allowed to scale. - **AI Observability & LLMOps.** See what the system did, what it cost, and what changed. - **AgentOps & Simulation.** Rehearse autonomous work before it touches the business. - **AI Security, Governance & Compliance.** Put security and accountability inside the system, not beside it. - **AI Infrastructure & AIOps.** Run the platform underneath the model, not just the demo. ### Technology ecosystem Tools and platforms we work with. A listing is not a partnership, certification, or endorsement. #### Models & Providers - **OpenAI.** Model APIs for application development and comparison. - **Anthropic.** Model APIs considered against the same task harness. - **Google Gemini.** Multimodal and long-context options in vendor evaluations. - **Meta Llama.** Open-weight models when ownership of the stack matters. - **Mistral.** Open and hosted models in build-versus-buy studies. - **Groq.** Low-latency inference options where speed is a requirement. #### Agent Frameworks - **LangGraph.** Graph-based agent flows when control flow must be explicit. - **CrewAI.** Multi-agent coordination patterns we will adopt only with a test harness. - **OpenAI Agents SDK.** Agent tooling when the surrounding platform is already a fit. - **LlamaIndex.** Retrieval and agent orchestration in document-heavy systems. #### Infrastructure - **AWS.** Cloud infrastructure for deployment, data, and operations. - **Google Cloud.** Cloud infrastructure, including model-adjacent platform services. - **Microsoft Azure.** Cloud infrastructure for enterprises already standardized there. - **Kubernetes.** Workload orchestration when the platform team expects it. - **Docker.** Portable packaging for services, evaluators, and jobs. #### Observability & Evaluations - **Maxim.** Evaluation and observability tooling we can implement, not a required standard. - **Langfuse.** Tracing and prompt operations, often alongside open telemetry. - **Arize Phoenix.** Open evaluation and trace workflows for teams that want to own the data. - **LangSmith.** Tracing and datasets when the LangChain-adjacent stack is already in use. - **OpenTelemetry.** The convention we prefer so traces are not trapped in one vendor. #### Vector Databases - **Pinecone.** Managed vector search for retrieval systems. - **Weaviate.** Vector search with a preference for portable deployments. - **Chroma.** Lightweight vector storage for early systems and tests. - **Qdrant.** Vector search when latency and self-hosting are part of the brief. #### Development - **Python.** Primary language for model applications, evaluation, and pipelines. - **Go.** Services where concurrency, deployment, and operational simplicity matter. - **TypeScript.** Product interfaces, gateways, and full-stack delivery. - **FastAPI.** Service boundaries for model-backed applications. - **React.** Interfaces for copilots, review tools, and operational consoles. #### Security - **AI red teaming.** Structured attempts to misuse a specific system before users depend on it. - **Guardrails.** Controls on retrieval, tools, and outputs, designed with the architecture. - **Adversarial testing.** Cases built to break assumptions about instructions, data, and tools. - **Security assessment frameworks.** A repeatable way to rate findings and retest them after a fix. ## Engagement models ### AI Advisory & Consulting Fixed-scope assessments, research, strategy and architecture consulting. Right when the question is still open: where to invest, what to build, which vendor or model to trust, and what the operating model should be. The deliverable is a decision and a design, not a standing team. ### Project-Based Implementation End-to-end delivery of AI applications, integrations and infrastructure. Right when the use case is named and the organization wants a system built, integrated, and handed over. Scope, acceptance tests, and the path to production are agreed before the build accelerates. ### Managed AI Services Ongoing management of evaluations, observability, AgentOps, LLMOps and AI security. Right when a system is live, or about to be, and quality, cost, security, or agent behaviour need an owner beyond the launch project. This is an operating engagement with a cadence and a remit. ### AI Services-as-a-Service Recurring delivery of domain-specific professional services using AI-powered operations. Right when the work itself is the product: a defined professional workflow, delivered repeatedly, with specialists accountable and AI carrying the volume. Only offered where that service line is in scoped delivery, never as a claim that every industry line is already running. ### Dedicated AI Engineering Teams Embedded AI architects, researchers, developers and specialists working alongside enterprise teams. Right when the enterprise wants senior AI capacity inside its own team for a sustained period: architecture, engineering, evaluation, or operations, working in the client's tools and cadence. ## Delivery methodology ### 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 ### 02 — Research Evaluate models, tools, vendors, architectures and feasibility. - Compare approaches against the task, not against a general leaderboard - Test build, buy, and integrate with the same criteria - Write down what would make the idea technically or economically unfit ### 03 — Architect Design the AI solution, infrastructure and operational framework. - Define data boundaries, tool permissions, and human approval points - Design for evaluation and tracing before the first production request - Choose an architecture the team can operate after the build ### 04 — Build Develop, integrate and deploy the required AI systems. - Implement the application, agent, or workflow against real integrations - Keep prompts, models, and tools versioned as the system takes shape - Deploy into an environment that resembles how it will be run ### 05 — Validate Evaluate quality, reliability, performance and security. - Run the evaluation harness against the tasks the system is allowed to do - Test security boundaries, including injection, leakage, and unsafe tool use - Measure latency, cost, and failure behaviour before scale is discussed ### 06 — Operate Monitor, optimize, maintain and continuously improve. - Trace requests, watch cost, and alert on the failures that matter - Review quality on a cadence, not only after an incident - Improve the system with the same harness that justified the launch ## Contact information - Email: hello@adanla.com - Email note: Placeholder inbox until a studio address is connected. - LinkedIn: https://www.linkedin.com/company/adanla - GitHub: https://github.com/adanla - Social note: LinkedIn and GitHub links are placeholders until official profiles are published. - Location: Working with enterprise teams remotely. ## Frequently asked questions ### What does Adanla do? Adanla helps enterprises research, strategize, architect, build, evaluate, secure, deploy, and operate AI systems. A second practice delivers professional work itself through AI-native workflows and human specialists, where that service line is actually in scoped delivery. ### How is this different from an AI automation agency? Automation of a single task is sometimes part of the work. It is not the practice. We stay across strategy, architecture, engineering, evaluation, security, and operations, and we treat domain services as designed operations with a human owner, not as unsupervised bots. ### Do you standardize on one model vendor? No. Recommendations are vendor-agnostic. We compare models, frameworks, and clouds against the task, the constraint, and the operating reality. Listing a technology on this site is not a partnership. ### Can you stay after launch? Yes. Managed engagements cover evaluations, observability, AgentOps, LLMOps, and AI security. Dedicated teams can also embed with an enterprise team. Operate is part of the lifecycle, not an optional extra we mention and then leave. ### How does an engagement start? With a conversation about the problem, the systems involved, and what a good outcome would be. Advisory work can stand alone. A build starts once the use case, the constraints, and the acceptance bar are clear enough to scope. ### Are the industry services already running as managed operations? No. Industry lines marked as scoped delivery can be designed and delivered per engagement, with specialists accountable. Lines marked as future service lines are directions we are preparing. We do not claim that every industry service is operational today. ### Who is the work for? CTOs, CIOs, chief AI officers, product leaders, founders, and operators who need AI implemented and run, not only explained. The same technical record is published for machines and engineers in Machine mode, llms.txt, and services.md. ### What should we bring to a first conversation? The workflow you want to change, the systems it touches, any constraint on data or risk, and whether you need a decision, a build, an operating partner, or a domain service. A finished specification is not required. ## Machine-readable resources - [LLMs.txt](https://adanla.com/llms.txt) - [Services markdown](https://adanla.com/services.md) - [Company JSON](https://adanla.com/company.json) - [Machine interface](https://adanla.com/machine)