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Teqnovos assesses generative AI ideas for feasibility and identifies implementation challenges before development starts. Its technology assessments compare solution options against performance and scalability requirements. The team can then plan the development work needed to move a viable idea toward implementation.
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An AI idea needs evidence before development. Test representative inputs against a defined output threshold and confirm that the required data is accessible. When several use cases compete for investment, compare expected business effect, data dependencies, delivery effort, and the cost of an incorrect result.
A successful demo does not establish production readiness. Review test coverage, exception handling, latency, operating cost, and fallback behavior before expanding access. For integration with existing systems, map data sources, API exchanges, identity permissions, and write actions.
When sensitive data or automated actions are involved, identify which sources the system can access, which outputs require human review, and which actions need explicit approval.
AI-Powered Elderly Care Monitoring Platform
Teqnovos built an AI powered elderly care monitoring platform that connected daily updates with alerts and caregiver actions. The platform helped care teams identify concerns earlier. It also improved follow ups and supported safer independent living for older adults.
Smarter Healthcare Data Mapping With Generative AI
The platform used generative artificial intelligence to speed up healthcare data mapping. It connected legacy records with FHIR workflows. Specialists could review uncertain matches before release. This reduced manual effort and helped teams complete integrations faster.
Automating Sales Outreach With AI-Powered Workflows
SalesHub.ai combines LinkedIn outreach with email campaigns and visitor tracking. Teqnovos built real-time data processing and automated messaging workflows. The platform achieved 99% process automation and reduced operating costs by 50%.
Building an AI-Powered Tender Management Platform
Orbidal collects tender data from hundreds of sources. It recommends suitable opportunities based on company history and expertise. The platform also generates ready-to-submit bid documents. This reduced preparation time by 80% and tripled the tender success rate.
Building an AI Platform for Clean Energy Compliance
1Climate uses Artificial Intelligence to collect regulatory data from federal and local sources. It delivers project-specific permit and tax credit requirements. The platform reduced data collection time by 80% and accelerated regulatory reporting by 70%.
AI consulting services address distinct needs, from checking whether a workflow suits AI to integrating an approved capability into existing software. Each engagement can focus on a specific business problem or combine related services.
For a defined workflow or AI proposal, this assessment examines the current task, its data, system access, and exception patterns. It identifies technical constraints and whether AI, conventional automation, or another software change fits the problem.
This service turns selected opportunities into an investment sequence. The AI strategy and roadmap connects use-case priorities with prerequisites, pilot scope, expected costs, and measurable business outcomes.
Generative AI consulting services identify tasks that involve creating, summarizing, extracting, or retrieving information. The work defines which model and workflow pattern can serve the intended users and data.
Architecture consulting defines how the model, data sources, APIs, identity controls, and application components interact. The design documents request paths, failure behavior, and expected system capacity.
This service examines repetitive workflows to separate deterministic steps from tasks that require interpretation of messages, forms, or documents. It defines how exceptions move to staff review.
Agentic AI consulting scopes multi-step tasks where a system may retrieve information or call approved tools. It defines permitted actions, approval points, escalation paths, and stop conditions.
A limited prototype tests representative inputs before broader development. Evaluation covers output quality, task completion, latency, and failure behavior against criteria agreed for the use case.
Implementation connects an approved AI capability to the target application, data sources, and APIs. Teams that need additional engineering capacity can hire AI developers for model development and deployment.
For deployed systems, this service reviews task quality, latency, cost, exceptions, and policy events. Findings guide model, prompt, retrieval, or control updates as usage and workflows change.
Responsible AI governance assigns decision owners, defines review triggers, and preserves evidence of how controls were applied. Control selection depends on the system’s jurisdiction, industry, data types, and use case. These controls can support applicable privacy, security, regulatory, and organizational requirements.
Assign policy decisions to named roles across product, security, legal, and operations. Set an escalation owner for high-risk decisions so exceptions reach a responsible reviewer.
Translate written policies into prompt and output checks that block violations or route them for review. The AI guardrail controls also map rules to agent actions and log violations.
Define which events pause an AI workflow for human judgment. Record the trigger, reviewer, and approval decision before processing continues.
Before release, test for prompt attacks, unsafe outputs, and access misuse. After release, track blocked prompts, unsafe outputs, and agent failures to identify when controls need adjustment.
Keep violation logs, approval records, and test results for review. These records let teams trace a policy decision from its rule to its control result and approval path.
| Engagement | Estimated budget | Typical timeline | Scope |
|---|---|---|---|
| AI strategy and feasibility | $6,000–$24,000 | 2–4 weeks | Review selected workflows, data sources, and system dependencies; deliver a prioritized AI strategy and roadmap with a defined pilot scope. |
| Prototype and validation | $20,000–$75,000 | 4–10 weeks | Build a limited prototype for one workflow; test it against representative requests, edge cases, and agreed pass criteria. |
| Production integration | $50,000–$250,000+ | 3–9 months | Integrate an approved AI capability with business applications, access rules, human approvals, and monitoring. |
Scope data preparation, integrations, model usage, and validation for a more grounded project estimate.
An AI use case depends on the task, the information available, and the consequences of an incorrect output. We connect these conditions to the business outcome so teams can choose a suitable approach and define what a pilot must prove.
Trace one case from its starting event to completion. Compare a routine path with an exception path. Note where work waits, returns for missing information, or depends on staff judgment. This shows whether the bottleneck sits in intake, handoff, or decision-making before AI consulting for businesses proposes a technical change.
Follow each required field to its source and owner. Sample records for missing values, conflicting versions, and stale updates. Separate data-quality gaps from access or integration constraints so the recommendation can identify whether the next step is data correction, a permission change, or a source connection.
Classify the task by its inputs and decision logic. Stable rules may suit conventional software, while unstructured text, image interpretation, or forecasts from historical patterns may call for a model. Compare each option with the current manual or software result to see whether AI would change the task outcome.
List credible failure cases and trace their effects through the workflow. A wrong output that triggers a correction differs from one that sends a customer message, approves a payment, changes access, or starts a safety action. Set review and escalation points where a person can limit the impact under responsible AI governance.
Generative AI consulting services distinguish text generation from retrieval and action. Retrieval augmented generation (RAG) grounds responses in selected sources; workflow automation executes fixed rules; an AI agent uses approved tools within defined task limits. A support assistant may combine retrieval with human escalation.
Choose a primary measure tied to the workflow and record its baseline before a pilot. Pair it with a quality or risk measure so faster handling does not hide more errors; for example, track processing time alongside correction rate. Set the evaluation sample, review period, and pass threshold for moving the pilot into the AI strategy and roadmap.
AI strategy and roadmap decisions compare candidate use cases side by side. These four review areas connect each candidate’s business case and delivery constraints to a roadmap action: advance, add prerequisite work, limit the rollout, or defer.
Compare the estimated operational gain with the process volume and current baseline. A candidate with meaningful volume and a measurable benefit can advance; an unsupported return estimate needs a limited pilot to gather evidence before broader funding.
Data access and quality affect whether a candidate can be tested now or needs prerequisite work. High decision complexity or frequent exceptions may require a narrower pilot, separate exception handling, or phased automation.
Estimate how much system work each candidate requires. A use case with substantial integration dependencies may need an integration milestone before the main build; a smaller footprint can fit within the pilot scope.
Rate the consequence of an incorrect result and the review required before an action reaches a user or system. Higher-impact cases may need a human-review milestone and limited rollout. The AI elderly care monitoring case study shows client-defined alert levels routing higher-priority concerns into caregiver follow-up.
Depending on scope, AI consulting services can produce reviewable artifacts for investment decisions, engineering handoff, and production ownership.
This pack records the workflow baseline, data and system owners, source locations, access constraints, and unresolved dependencies. Its dependency map connects each required dataset or API to the owner and approval needed before development begins.
The roadmap includes a weighted scorecard for candidate use cases, with evidence and assumptions recorded for each score. It also identifies prerequisites, decision gates, and the proposed scope for the selected pilot.
This pack contains a system context diagram, component responsibilities, data flows, integration requirements, and the model’s task and input-output specifications. It also documents fallback behavior and conditions that require human review.
The validation record contains versioned test cases for expected, edge, and failure inputs, along with pass criteria and observed results. An issue register tracks each finding’s severity, owner, and retest status.
The handover documentation covers deployed components, environment-specific settings, monitoring signals, alert destinations, response ownership, and rollback steps. Operations teams can use the runbook to handle incidents and schedule post-launch reviews.
Define the operational problem and task being considered for AI. The team records affected users, the current baseline, the process owner, and excluded work in an opportunity brief that states the target change and assumptions.
Review whether the required data and systems support a pilot, including access gaps, data-quality issues, integration dependencies, and technical constraints. The client receives a readiness note with prerequisites, assumptions to test, and conditions for proceeding.
Apply the agreed scorecard to shape the AI strategy and roadmap. The resulting backlog orders candidate use cases and marks each for a pilot, prerequisite work, or deferral, with an owner and decision rationale.
Translate the selected use case into workflow and system specifications. The team defines the model’s role, data movement, interfaces, access boundaries, and human handoffs. The client receives an architecture diagram, integration requirements, and acceptance criteria.
Build a limited version around one prioritized task and test representative requests, including edge cases. The pilot record compares observed outputs with pass criteria and lists open issues to resolve before validation.
Test the pilot against agreed criteria in realistic workflow conditions, including abnormal inputs and the intended user review path. The client receives a validation report with pass or fail evidence, outstanding defects, and a production recommendation.
Release the approved solution into the target environment after checking system connections, role access, monitoring signals, and rollback steps. The handoff includes a release record, operating runbook, and named ownership for agreed controls.
Compare live task results and exception patterns with the pilot baseline. Review drift, new inputs, and usage changes, then give the client a prioritized improvement backlog with supporting evidence, an owner, and a next review trigger.
Map the data access, approval steps, and failure paths involved in one business process.
Teqnovos has won the hearts of its customers through hard work and dedication. Our remarkable web and mobile services have made us an industry-leading IT company.
From the first discovery call, they understood our space. The final app is sleek, bug-free, and our users love it. We’ve even received investor praise for the product quality.
Brandon
Their developers were senior-level, communicated clearly, and integrated seamlessly with our in-house team. We extended the team twice and now consider Teqnovos a core development partner.
Teresa
The HR chatbot now handles over 70% of repetitive queries and employee satisfaction scores have improved significantly.
Jason
They are exceptional developers and will go above and beyond to deliver a quality app. If you are looking for a reliable team, excellent communication, and benchmark delivery – this is your team!
Adam Adrello
They are professional and kind and very communicative They built for us an iOS and Android app along with app Web admin for user registration management and the job is completed as promised. Highly Recommended !!
Karim Massoud
This was a complex project. The communication and availability of their project manager Ankur was excellent. The team showed excellent technical skills and was able to code custom solutions as needed. If you’re considering a remote developer, I highly recommend Teqnovos.
Yoram Lepair
Teqnovos did a great job on setting up my technical training site focused on CRM and AdTech at adspiromarketing.com – They were very responsive, available every weekday for our calls and had written summaries of work done for review prior to any of our calls.
Francisco Guerrero
Teqnovos brought structure to MALOUM’s payment and payout workflows. The team connected payment events, creator balances, payout processing, and reconciliation in a way that made exceptions easier to trace. They translated complex requirements into practical technical decisions and created a clearer foundation for our creator platform.
Fabian Fröschl
As an AI consulting services company, Teqnovos connects recommendations with implementation across custom software, generative AI, and system integration. Its documented projects include healthcare data mapping, tender recommendations, and sales-workflow automation.
Define pass criteria before testing. The review can check representative requests, edge cases, output quality, system behavior, and unresolved issues against those criteria.
They are listed separately unless the proposal includes them. These costs can change with request volume, model choice, storage, and hosting requirements.
Yes. The engagement can identify data gaps and access constraints that affect the use case. If unreliable data could produce misleading outputs, the roadmap can include data cleanup before a pilot or production release.
Share the target workflow, example inputs and outputs, relevant data sources, connected systems, and the outcome the team needs to measure. These details help scope the work and identify dependencies before implementation.
A strategy engagement may take 2–4 weeks. A prototype may take 4–10 weeks, while production integration may take 3–9 months. Data access, integration work, and review requirements can extend those timelines.
AI strategy consulting services define the use case, roadmap, and pilot scope. Prototype development and production integration are separate unless those engineering tasks are included in the proposal.
A focused AI strategy and feasibility engagement is estimated at $6,000–$24,000. A prototype with limited engineering may range from $20,000–$75,000, while production integration may start at $50,000. The final estimate depends on data preparation, system connections, testing, and governance requirements.
